Houssem Chemingui

Ines Gam

Raul Mazo

Camille Salinesi

Henda Ben Ghézala

Raúl Mazo

Henda Ben Ghezala

ConfiLog: An approach to Guide Product Line Configuration Processes based on Configuration Traces

Keywords: Product line engineering, configuration process, assistance, recommending, user's actions, goal-question-metric, trace mining

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INTRODUCTION

Mass customization is a phenomenon in vogue and widely adopted by companies. Major industry clusters work hard to develop personalized offers that meet their customers' distinct preferences [START_REF] Da Silveira | Mass customization: Literature review and research directions[END_REF]. At the same time, they work to improve the efficiency of their production processes. Indeed, product lines (PLs) have been a strategy that corresponds to these needs. PLs have become a promising approach for the development of configurable systems that are built from a set of reusable components. PLs aim to explore the similarities and variabilities in a collection of products that share a specific domain [Northrop, 2002]. In practice, the variability and commonality between products are captured by a Product Line Model (PLM) like a Feature Model (FM) [START_REF] Kang | Feature-Oriented Domain Analysis (FODA)Feasibility Study[END_REF], an Orthogonal Variability Model (OVM) [START_REF] Pohl | Software Product Line Engineering, Foundations, Principles, and Techniques[END_REF] or a Dopler model [START_REF] Dhungana | The DOPLER Meta-Tool for Decision Oriented Variability Modeling: A Multiple Case Study[END_REF]. These modelling languages, such as the FM used in this paper allow distinguishing between variable products that is possible to configure from the line. Some PLM features define points of variation and their role is to permit the instantiation of different products by selecting specific PLs functionalities. A fundamental task in PLs is the process of selecting and/or deselecting features from the FM in order to construct a new product configuration. Usually, when the number of features increase in a PLM, the number of potential products increases also. Thus, industrial-sized PLMs with hundreds or thousands of features can make the configuration process impractical. For example, product lines in the automotive industry can hold up to 10 21 possible configurations [Astesana et al., 2010] that involve multiple stakeholders [START_REF] Mendonca | Decision-making coordination in collaborative product configuration[END_REF]. Indeed, the configuration process becomes quickly complex when increases the number of decisions to be made, the fact that these decisions must be taken in a predefined order that respects user preferences on the one hand and the model constraints on the other hand. Assistance and automatic propagation of some choices are needed to improve the configuration correctness and quality when dealing with many possible combinations.

Thereby, our purpose is to reason on complete or incomplete users' traces of configurations recorded in logs in order to have an idea of : (i) Which informations can be useful to guide the PLs configuration? (ii) How to make use of previous configurations traces and interpret knowledge from it. (iii) How to use this knowledge into the future configuration processes? (iv) According to traces, what guidelines can be used to assist the configuration? Several analyses have to be performed in order to answer to the previous questions. In order to do so, we configure PLMs, we import the configuration logs and analyze them by process mining techniques [START_REF] Aalst | Process Mining: Discovery, Conformance and Enhancement of Business Processes[END_REF]. The approach presented in this paper uses process mining techniques applied to the logs captured from user's' configuration actions. The primary contribution of this research is to assist users during the product configuration by providing (according to previous traces) them an easier and personalized configuration. Answering to this issue, industries dealing with PLs can improve the configuration experience of their configuration tools and improving the satisfaction of who is configuring new products. Mining traces and analysing then after allow us to capture the occurrence statistics and the timestamp measurement of user activities and their effects which allow us to develop some guidance metrics to improve the configuration process. However, the identification of metrics it is not a simple task since too many metrics can be identified for different goals. This problem tends to overwhelm process designers in their interpretation and analysis. Therefore, it becomes necessary to investigate methods of identifying the pertinent metrics that we need to assess and improve our processes. In software engineering, many frameworks were proposed to address this issue. Examples of those are: the Goal Question Metric (GQM) approach [Basili, 2005], Personal Measurement Software (PSM) [Card, 2003-a p738], and the framework developed by [Fenton and Pfleeger, 1997] in Software Metrics a Rigorous and Practical Approach. In this research, we are interested in the GQM framework.

The remainder of this work is organized as follows: Section II surveys a pertinent literature related to PL configurations and Process mining. It elucidates also how it possible to mine PL traces. Section III outlines the challenges of process mining in the context of PL configuration and proposes our GQM guidance approach. Section IV presents preliminary results through our running example. Section V presents related works while the concluding section discusses research perspectives.

II.

THEORETICAL BASIS AND CONFILOG POSITIONING In this section, we introduce, first, the PL paradigm and its concepts. Then, we highlight the main goals and concepts of process mining. In subsection C, we describe related recommendations methods related to our subject. And finally, we propose an overview of our approach describing the main lines that we handle with in this research.

A. Product line concepts and interests

A PL is "a set of systems that share a common, managed set of features satisfying the specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way" . The realization of a PL is based on two 1 distinct processes: domain engineering and application engineering. The domain engineering process is designed to capitalize and document all artefacts (ie. code, software components, libraries, etc.), also called assets, necessary to produce software systems in a clearly identified domain (e.g., enterprise management software and mobile phone operating system). These artifacts are organized in such a way as to determine which are indispensable, interchangeable or dependent on others, within the same architecture. The application engineering process involves reusing existing artifacts to produce a new product that meets specific needs. The process of choosing artifacts to reuse is also called the configuration of a product. A configuration represents a product of the PL, completely or partially defined, and it contains all the elements of the PL that are selected according to the requirements of the users and the constraints prescribed during the specification and modeling of the corresponding PL. A configuration can be considered also as a tuple (V, D, C) where V = {v1, v2, ..., vn} represents the set of finite domain variables of the corresponding PLM, D = {dom(v1), dom(v2), ..., dom(vn)} represents the set of corresponding domains of each variable, and C represents a set of configuration constraints.

B. Process mining concepts

Process mining is a set of techniques that enable to discover, monitor and improve processes through the extraction of information from event logs available in information systems of today [START_REF] Aalst | Process Mining: Discovery, Conformance and Enhancement of Business Processes[END_REF]. Process mining was defined by Günther [START_REF] Günther | Process Mining in Flexible Environment[END_REF] as an area of research that is concerned with posteriori analysis of business processes, based on event logs of their execution. Its purpose is to extract global information and highlighting the different aspects of the process from these event logs. Hence, the process mining is adopted by some organizations that do not have enough informations about what is happening in their activities. Figure 1 recapitulates the general principle of process mining. The starting point for process mining is an information system that records executions traces of processes and store them in event logs. An event log can be seen as a collection of cases and a case can be seen as a sequence of events [START_REF] Aalst | Process Mining: Discovery, Conformance and Enhancement of Business Processes[END_REF]]. Each event contain informations about its execution like the timestamp, the activity that involve, the originator of this event, etc. According to the event logs, process models can be generated with differents concerns and techniques. during the application of process mining techniques. There are three fundamental techniques of process mining [START_REF] Aalst | Process Mining: Discovery, Conformance and Enhancement of Business Processes[END_REF]: (i) To mine process models (Discovery); (ii) To identify, explain and quantify process differences when existing process models are compared with the discovered ones (Conformance); and (iii) to apply changes to the existing process models based on the discovered ones (Enhancement). Some techniques aim to go further and use traces to suggest which tasks may follow a current activity [START_REF] Mobasher | Automatic personalization based on Web usage mining[END_REF], [START_REF] Schonenberg | Supporting Flexible Processes through Recommendations Based on History[END_REF]. Based on traces and generated models, predictions and recommendations can be introduced on ongoing and potential processes. The collaborative recommendation predicts the usefulness of a product for a particular user based on evaluations of this product made by other users. To recommend a product for a specific user, the system has to calculate the utility of the product estimated by the users who are similar to him. Therefore, to recommend configurations to a user, we have to look for users with similar choices for configurations. The most appreciated configurations by these users will be recommended to the user. The recommendation based on the content makes possible to recommend products similar to those that the user has already appreciated. It allows to predict the utility of a product based on the user-assigned utilities to similar products [Adomavicius & Tuzhilin, 2005]. This similarity is calculated from the product descriptions and the user profile in terms of the characteristics. For each characteristic a weight is associated indicating its degree of importance. Thus, a weight vector is associated to each product. The user profile can be constructed from previous evaluations or actions. In our case, the content-based recommendation is reflected by the fact that we analyze the user actions in order to recommend to him similar alternatives to his preferences. A third type of recommendation is based on knowledge about characteristics, user preferences, and the context. The knowledge-based recommendation rely on a feedback loop in which the user requirements and preferences are gathered. When the user preferences are difficult to identify or gathered at once then the feedback loop could be employed multiple times.

In our research, we are interested in the hybrid recommendation that combines two or more types of recommendations to provide better performance. Several researches such as [Adomavicius & Tuzhilin, 2005] evaluated the performance of the hybrid with the pure collaborative, content-based and knowledge-based methods. Then, they demonstrated that the hybrid methods can provide more accurate recommendations than pure approaches. Generally, Hybrid recommendations are used to overcome some problems if we use a unique method of recommendation [Burke, 2002]. The role of process mining, is to support the decision making by enabling process guidance. Moreover, process mining is interesting in all recommendation methods as it can reveal and visualize the relationship systems, contents and users. Nonetheless, the reasoning processes for developing a recommender strategy is a separate research objective to our current one.

D. When process mining meets product line configurations?

To apply process mining techniques in the context of PL configuration, we propose, CONFILOG : an approach that aims to provide assistance during the configuration of products. Figure 2, shows an overview of this approach that consists of two main compartments notably a front office process and a back office process. In the front office, a set of choices is made by users during the PL configuration through the tool interface. Before configuring a product, generally users have always a full or a part of the desired features of the product to configure ( "Product Model" Ⓐ ) but their are constrained by a "PL model" that they have to respect. The back office is a set of processes when happen guiding directives. From the front office, all the users' traces are captured, extracted and recorded in event logs. Throughout this process, ETL (Extraction, Transformation, and Load) mechanisms cooperate in order to treat configuration traces made by users, transform their structure, load logs and filter pertinent data according to intentions [START_REF] Rodic | Generating data quality rules and integration into ETL process[END_REF]. A continuous learning is planned inside the "Logs Data Collector"

Ⓑ that take as input, a collection of unfiltered logs resulting from configuration processes. This process aims to save relevant logs which will serve to precise guidance issues.

Mining past PL configuration processes is mainly based on capitalized event logs resulting from Ⓑ. Throughout process mining treatments, a definition of mining criteria and queries is important. Monitoring flows and relationships is also commendable. The choice of process mining algorithms depends on the desired intentions and ends. Transformed traces will be considered as parameters of the process mining algorithms. The outputs of these algorithms are "Process models" Ⓒ that produce a repository used in order to interpret models and provide guidance answers. Given answers by process mining, are injected in a " Goal question metric" [START_REF] Basili | Goal/Question/Metric Paradigm[END_REF] repository Ⓓ (cf. section III.A) that capitalize answers for specific guidance questions using specific metrics. Take into account Ⓐ and Ⓓ guidance indicators are provided to make the configuration task more interesting, flexible and guided.

Figure 2. Overview of the CONFILOG approach

We are convinced that this approach is conveyed and portable regardless of the PL configuration context. In this paper we target to provide guidance during the configuration of PLs. Therefore, the tool engineering is considered as a PL configurator and the users are constrained by a PL model. However, the portability and the genericity of this solution seems promising. This approach can be applied to guide complex business processes of different contexts. Based on mining traces of users and correlations between these traces, it is possible to generate process models. Then, using these process models, the guidance will be monitored through the goal-question-metric method to provide specific answers for specific guidance goals.

III. THE CONFILOG APPROACH UNDERPINNINGS

This section presents the CONFILOG approach to guide customers during the configuration of the PLM according to the traces of previous configurations. The first part of this section introduces the CONFILOG framework. The second part illustrates the process of CONFILOG and the third part shows how to use the proposed approach according to a running example.

A. The CONFILOG framework

We propose the following framework to offer a solution for assisting users during a PL configuration based on previous traces. In this sense, we focus on the identification of the relevant data for logging, extracting and mining. In the PL configuration context, we decided to structure the event logs as in Table 1.

Table 1. Definition of the event logs structure We introduce solutions to express where and how guidance strategies can be developed to face configuration difficulties. To highlight our assistance goals, we need to make sure that we make the right answers to the right questions using the right metrics. The GQM (Goal Question Metric) method is one simple way of making sure that the metrics we collect is closely tied to the goals. The GQM was proposed as a framework for defining and interpreting software measurement. It was expanded later with many other concepts and become a result of many years of practical experience and academic search. Figure 3 shows the hierarchy levels of the GQM model: goals, questions and metrics. The upper level presents goals (conceptual level) that have to be defined. Each goal has to be operationalized by generating questions that help to understand how to meet it. Questions (the operational level) try to characterize the object of measurement in a context of a quantified issue. The lower level presents the metrics (the quantitative level) that need to be collected to answer the questions. The some metrics can be used to answer more than one question.

Attribute Description

Figure 3-GQM Model structure [START_REF] Basili | Goal/Question/Metric Paradigm[END_REF] To proceed with the GQM approach, goals should be identified initially. Considering our current context, the guidance type that we try to provide will match the goal in the GQM model. In the second step, questions have been developed to assess each goal. We carefully created the questions by refining the goal into several questions and ensure the questions we created are measurable. As several questions were developed, it is possible to get a complex guidance support to integrate throughout the configuration process . Hence, we decided to categorize them by combining the goals that will affect others. Finally, we consider that operations that we make, to answer to a given question, are the set of metric that provide the information to answer those questions. In this case, we will refine all the questions into metrics. As a result, five guidance modes act as goals in the GQM model :

Goal 1: Provide user-friendliness of the configuration task from the process miner viewpoint.

Goal 2: Reduce the required time to reach a valid configuration from the process miner viewpoint Each goal has a purpose, an issue, an object, a stakeholder viewpoint and at least one question. Each question needs more than one metric to evaluate in order to have an answer. More details about guidance goals, questions and metrics are in the following subsubsections.

GQM Model 1 : Table 2 describes the GQM model for the following goal: "Provide user-friendliness of the configuration task from the process miner viewpoint". User-friendliness is considered as as functioning in the best manner with least waste of user effort. In order to achieve this goal we answered to the Q1,n questions, with n= 1..8, using the M1,n,m metrics. GQM Model 2 : Table 3 describes the GQM model for the following goal: "Reduce the required time to reach a valid configuration from the process miner viewpoint". Required time is considered as as performing in the best manner with least waste of user time. In order to achieve this goal we answered to the Q2,n questions, with n= 1..5, using the M2,n,m metrics. GQM Model 3 : Table 4 describes the GQM model for the following goal: "Maximize the flexibility and efficiency of configuration alternatives to the lost users from the process miner viewpoint". Efficiency and flexibility are vital to provide users an attractive configuration process. Helping lost users during their configuration tasks has to answer to the Q3,n questions, with n= 1..6, using the M3,n,m metrics.

GQM Model 4 :

Table 5 describes the GQM model for the following goal: "Provide a maximum of personalization of configuration suggestions from the process miner viewpoint".

Capturing user identity, habits and past practices is very important in order to make them loyal. In response to this goal, we answered to the Q4,n questions, with n= 1..4, using the M4,n,m metrics. Selling maximum of stock and/or minimizing production costs, etc. are interesting goals for the company. In response, we answered to the Q5 question , using the M5,m metrics.

B. The CONFILOG process

The CONFILOG process prescribes a sequence of phases to produce user assistance and improve product line configurations. Nevertheless, in our analysis, we were faced to different users concerns and contexts. Several guidance answers are possible from process mining algorithms. Therefore, we organized all these answers in the GQM framework in order to achieve our challenges. Each guidance scenario has a goal, questions and metrics that are extracted from logs. Figure 4 shows the phases of this guidance process. We are convinced that this process is portable also and can be applied in other process guidance contexts regardless of the product line configuration process. Phase 2: "Relate user requirements and guidance goals" This phase relates all the user concerns with the corresponding guidance goals. First, identify an instance of the guidance goal. Second, refine the goal in a set of specific goals that satisfies the main goal individually or all together. (cf. section III.A)

Phase 3: "Identify corresponding guidance questions" This phase identifies from the CONFILOG framework, what are the most corresponding questions (cf. section III.A) that they meet the user concerns and the selected guidance goal.

Phase 4: "Identify corresponding guidance metrics" This phase identifies from the CONFILOG framework, what are the metrics (cf. section III.A) that have to be exploited in order to answer to the question selected in the third phase.

Phase 5: "Logs extractor" This phase implied the extraction and integration of data from the configurator. For each user action several attributes have to be extracted (cf. section II.C) . Furthermore, knowledge is transferred tacitly during this phase through data filtering and comprehension. This guided us further in understanding how we could generate configuration support.

Phase 6: "Process Miner" This phase consists in applying process mining and analytics techniques (cf. section II.C) to the event logs. The aim of this stage is to provide answers for questions, asked in phase 3, using metrics, identified in phase 4, in order to achieve guidance goals, identified in phase 2.

Phase 7: "Diagnosis and Validation" This phase targets the users of our configurator. Our process should pass by the diagnosis and validation of the findings. Thus, the objectives of the diagnosis phase are : to interpret the mining results correctly, identify interesting or particular aspects, and propose other guidance questions for further iterations. The objective of the validation is to compare the findings to the expectations of the process stakeholder before providing him the guidance.

Phase 8: "Scenario support and guidance" This phase aims to provide the users what they are waiting for. This subprocess, is a transitions from statistics and mining results to real proposed features, configuration paths and/or ready configured products.

Once support is provided to users, the system's analyst review the precision and usability of the result. If additional precisions are required we go back to phase 3. Else, if others user concerns are detected or added we go back to phase 1.

C. How to use the CONFILOG approach through a running example

To illustrate CONFILOG , we provide real guidance scenarios in the online commercialisation of software as a service domain. Figure 5, shows the feature model [START_REF] Kang | Feature-Oriented Domain Analysis (FODA)Feasibility Study[END_REF] of the provided website product line [START_REF] Mendonca | Decision-making coordination in collaborative product configuration[END_REF] which is composed of 25 interrelated features. For example, the "Performance" Feature is a mandatory feature but the feature "Additional Services" is optional. Adding to that, if the feature "Reports" is selected in a configured product, the "File" and "Milliseconds" features must to be selected also. The selection of the feature "Milliseconds" excludes the selection of the feature "Https". The goal is to offer personalized solutions to enterprises that are looking to configure websites in different market segments. To configure a website from this line, the customer interacts with an interface that allows him to select the website characteristics according to his preferences. In fact, If the user is looking for a premium website, the performance have to be powerful (on "seconds" or "milliseconds") by automatic propagation of choices. On the other hand, several decisions have to be made by the user such as, his preferences for the "Content" branch, etc.

In a such e-commerce domain, it is common to offer products that attract new customers by identifying personalisation scenarios. Let us consider a configurator that is able to identify the best-selling websites in 2016 for textile companies in France, the best selling websites for particulars or the full options configured websites. According to traces, it is possible to identify also the historic of a specific user and his behavior with a link to the detailed product description. Suggest configuration processes and paths based on similarity measures between previous configuration is another guidance way that consists in answering the question. In our GQM model, it consists in answering the appropriate question using the adequate metrics (cf. section III.A). In our running example, we used the CONFILOG process in order to answer to all these configuration guidance requirements. Mining the old configurations makes possible to improve the next configurations and guiding the user's choices.

Phase 1 in action: We are convinced that providing an easy configuration process and maximize the personnalisation in the same time, are the two important requirements that users are looking for when they are configuring a website from this line.

Phase 2 in action:

To answer for these two user concerns, we have to identify what are the most oriented goals that match these guidance needs. Therefore, we selected from our GQM framework Goal 1:

"Provide user-friendliness of the configuration task from the process miner viewpoint" and Goal 4: "Provide a maximum of personalization of configuration suggestions from the process miner viewpoint" .

Phase 3 in action: During this phase, the system analyst has to select the most pertinent guidance questions that correspond to the user requirements and the context. In our running example, we selected Q1.2. What are the processes that include fashionable products?, Q1.3.

What are the processes that include the bestseller products?, Q1.4. What are the processes that include the worst-seller products?, Q1.5. What are the processes that include full option products?, Q1.6. What are the processes that include minimum option products?, Q4.2. What are the processes of a specific user based on his history? a nd Q4.3. What are the users that have similar configuration intentions?

Phase 4 in action:

In order to answer to our selected guidance questions (in phase 3), we have to evaluate the following metrics using traces extracted from previous configuration processes : M1.2 Phase 5 in action: Evaluating metrics selected in phase 4 is based on traces (cf. section III.A). In our running example, we captured unfiltered traces from several users. Nevertheless, ETL measures were applied in order to select only pertinent traces that correspond to complete configurations. As attributes we selected all the informations that we planified (cf. table 1): IDConfig, Originator, FeatureName, FeatureType, FeatureDecisionType, FeatureDecisionStep and Timestamp. We are convinced that these attributes are useful and permit to evaluate the metrics above.

Phase 6 in action: After extracting traces about previous configuration, we used a process mining tool. We imported all these traces in a required format. The tool allow us to have frequency and performance statistics about configuration processes and it become possible to identify: f ashionable websites, bestseller websites, worst-seller websites, full option websites, minimum option websites, common last processes of x, users that have similar configuration intentions to x . How to get these answers? Further details about mining steps, queries and comments are in section IV.

Phase 7 in action:

In the one hand, we have descriptive statistics of previous configurations (Phase 6). In the other hand, we have guidance expectations that we have to meet. This stage is done by the user analyst that validates and diagnoses the usefulness and the sufficiency of process mining outputs in the current process. A combination and a synchronisation of all results are important.

Phase 8 in action:

In this stage, the system analyst translates results of phase 6 to real guidance assets. For example, according to analysis, the feature Additional Services was selected in more than 70% processes. The manner and timing of recommending and displaying this mining result for next users is the aim of this phase.

IV. PRELIMINARY EVALUATIONS AND RESULTS

To perform our experiments, several steps have to be taken and they have to be in a certain order. Thus, a process for how to perform our experiments is needed. Figure 6 illustrates our experiment process which is divided into the following main activities. Scoping is the first step, where we scope the experiment in terms of goals. Planning comes next, where hypothesis, context, variables of the experiment is determined and the instrumentation is considered. Operation of the experiment follows from the design. In the operational activity, measures are collected which then are analyzed and evaluated in analysis and interpretation .

Figure 6. Experiment process inspired from [Wholin et al 2012]

A. Experiment Scoping (Why?)

The starting point is insight, and the idea that would be a possible way for evaluating whatever we are interested in. We planified the following experiment goal with a structure of the GQM method to determine goals:

"Provide solutions to users For the purpose of guidance With respect to the PL constraints In the context of PL configuration."

B. Experiment Planning (How?)

The planning phase determines the foundation for the experiment and how the it was conducted. The input of the planning phase is the goal definition. The planning experiment can be divided into 4 steps:

Context Selection: Our context of experiments is a Website configuration from a PL described in section III.C. Hence the experiment is conducted by customers that they are configuring a website from the line.. The experiments addresses a real problem, ie. the differences in performance and understanding of the domain, user differences profiles, full model constraints, etc.

Hypothesis formulation: An important aspect is to know and to formalize state clear what is going to be evaluated. This leads to the formulation of hypothesis. Here, it has been chosen to focus on two hypothesis : a PLM that we have to respect its constraints and a product model that contain the product that the user have in mind.

Variable Selection: The variable of our experiments is the features of our PLM and the traces captured after each user interaction.

Tooling instruments: Several tools like VariaMos [START_REF] Mazo | VariaMos: an extensible tool for engineering (dynamic) product lines[END_REF] and SPLOT 2 3 [START_REF] Mendonca | software product lines online tools[END_REF] are proposed to configure PLs. These tools offer the possibility of configuring PLMs interactively with the user and recording latter the traces of a given configuration but not in the suitable format that we need. In process mining, two tools are reported to be extensively used: ProM [START_REF] Dongen | The prom framework: A new era in process mining tool support[END_REF] and DISCO 4 5 [START_REF] Gunther | Disco: Discover your processes[END_REF]]. ProM contains a variety of mining algorithms but is less suited for handling very big event logs. On the contrary, DISCO is a commercial tool that can handle big data. Considering that, we opt to use DISCO in the current experimentation.

With this tool it is also possible to create insightful process maps and statistics automatically.

C. Experiment Operation

Our experiment has been planned and in this step it must be carried out in order to collect the data that should be analyzed.

Preparation: The users were not aware of what aspects were going to be studied. They configured products that goes with their preferences.

Execution:

The experiments was executed over 4 days, during which the product line model was configured 46 times. After 46 website configurations, we perceive that 1257 events (Feature selection or rejection) have been produced and recorded into the CSV ( Comma-Separated Values) file. Each configuration contain more than an event. Every event corresponds to the selection or the rejection of a given feature.

Data validation: Data was collected from 36 users. Many users configured more than one the PLM because we treated 46 configuration in our logs. We treated all traces, even traces coming from incomplete configurations. We are interested to listen to all user actions.

D. Experiment Analysis and Interpretations

Several treatments and interpretations can be held depending essentially on the intentions behind mining. Answers to the guidance questions are based on frequency and performance analysis. In this section, we focused on the questions of the first and the fourth goal of our approach. Further experiments details and answers to all the questions mentioned in section III are treated in the technical document below . We created also a

First analysis discussions : According to the traces, a map model of the 46 website configurations processes is generated automatically using DISCO. Figure 6 shows a part of this map model that is highlighting frequent decisions: The start of the process is illustrated by the triangle symbol at the top of the process map. Similarly, the end of the process is illustrated by the stop symbol. Activities are represented by boxes (selection: 1 or rejection: 0 of a given feature) and the process flow between two activities is represented by an arrow. Dashed arrows point to activities that occurred at the very beginning or at the very end of the process. The absolute frequencies are displayed in the number at the arcs and the activities. The thickness of the arrows and the coloring of the activities visually support these numbers. From the map model, it's validated that 46 configurations took place. The color of a feature varies according to its frequency in the traces. It is possible to recommend the dark and ignore the light colors. We can calculate the percentages of passage from one feature to another. From that point, metrics can be proposed, for example a threshold of 20%. Let's agree, that less than 20%, we can judge that the given feature is not too relevant. And vice versa, for example + 80% a feature can be judged to be relevant. Every PLM feature is presented in the map model by two nodes. If a feature has a single node that means that it is mandatory in the model. And it will appear in each configuration for example "Website", "Performance", "Webserver", "Content", "Static"; "Protocol".

In our running example, the mean frequency of activities is equal to 50.28. In fact, in a global context, activities with more than this threshold have to be recommended. In addition to that, there is 36 resources originators of traces. This reflects that from 46 configurations there are users who have done more than one configuration. The mean frequency of events according to users is equal to 29.23. We can judge if a user has produced more than 29 events, it is an active user. Less than 29 events, he is a simple user.

In 1257 events, 678 features were selected (53.94%), 517 were rejected (41,13%) and 62 features redefined by backtracks (4,93%).

The performance analysis involves the time measurement criteria. The first configured feature was "Minutes" in 20 processes, and "File" in 11 processes. This reflects that the feature "Minutes" is important and numerous users tried to start their configurations with this feature. The last configured features were "Database", "Reports", "Banners", "Image" and "File". According to the captured traces, the average configuration duration is 22.6 minutes. The capture of traces is during the period from 25/05/2016 at 14h11 until 29/05/2016 at 11h58. A configuration that lasts more than 22 minutes can be considered as a long configuration. In our study, the longest configuration lasted 1 hour and 39 minutes, the shortest lasted 3 minutes. Answers to the question asked from the beginning (cf. Section III/A) become available thanks to the frequency and performance analysis.

Analysis interpretation and GQM recommendations:

After analyzing previous website configurations, we detail answers to the guidance questions selected after phase 2 of our process (cf. section III.C). Answers to all the questions are available in our tutorial cited in reference 7. Table 7 contains 6 columns: The first column defines goals. In our processes we selected : Goal 1 and Goal 4. The second columns presents the question selected in order to achieve a given goal. Column 3 corresponds to the evaluated metrics that allow us to have a guidance answer. The fourth column presents the exploited traces taken into account to evaluate the metric, then Ai corresponds to the i attribute number (cf. table 1).

Column 5 illustrates some comments about queries and filtering treatments that our process miner used to answer to the question. Recommendation results are shown in the last column. ---------------------------------Processes with Id Config: 36 and 37

Users : Ali + Melanie

From previous configuration of websites, assistance guidelines become available thanks to to the mining treatments. In this section we focused only in two goals : Goal 1:

"Provide user-friendliness of the configuration task from the process miner viewpoint" and Goal 4:

"Provide a maximum of personalization of configuration suggestions from the process miner viewpoint" . Combining these results enhance and assist users during their future experience and offer to them an easier configuration task. For example, from Q1.2, Q1.3, Q1.4, Q1.5, Q1.6 the website provider can offers customers a specific discount for fashionable websites or highlights from the beginning the best sellers and/or premium configured websites. Allowing users to follow their last configurations processes ( Q4.2 ) is also a kind of guidance by showing them their last choices and preferences. It is possible also thanks to the Q5.3 to suggest paths based on similarity measures between users.

V. RELATED WORK AND DISCUSSION In PL configurations that offer a large number of possible combinations it is difficult and time-consuming to produce a coherent configuration that respect the PLM restrictions and meet the user preferences in the same time. A solution is to help the user through an interactive process that reduces the number of decision that have to be done in each step.

As far as we know, there are no studies that explicitly attempt to apply process mining on PL configurations. Whereas many other works tried to capture configuration traces and exploit them in order to enhance the PL processes. We consider this kind of research as an implicit proposals of mining configuration processes. In this sense, [Chastel et al., 2015] proposed to suggest to users the next action when they are building a configuration. The authors used the result of clustering to suggest actions. They construct, later, the trace of the actions carried out and then by classification, using the same measures as for the construction of the clusters, they determine the closest cluster. Other works [Triki et al., 2013] proposed a recommendation approach combined with the product line configuration. The idea is to combine knowledge-based recommendation and the content-based recommendation. In other words, it is a combination between the dynamic recommendation and the similarity measure. The recommendation is based on textual data of previous configurations.

Moreover, it is possible to guide the configuration task by using heuristics that aim to reduce the number of user actions and/or minimize impact of decisions [START_REF] Mazo | Recommendation Heuristics for Improving Product Line Configuration Processes[END_REF]. To deal with complex configurations and also to integrate intelligent configuration processes, the notions of views and workflows have been defined [START_REF] Abbasi | A toolset for feature-based configuration workflows[END_REF][START_REF] Hubaux | Formal Modelling of Feature Configuration Workflows[END_REF].

Our research aims also to provide recommendations during the configuration process and to suggest to the user next actions. We are simply handling with user traces but we are interested with techniques of process mining. Analyzing process traces and interpreting results aims to recommend personalized decisions for users and make easier their configuration task. This axis corresponds to the subject of our current and future research.

Nonetheless, several scientific questions are arisen concerning the the consequences of this approach for bsidered at design time. In our research, in order to provide guidance, we capitalized traces coming from the PLMs. If the PLM evolve, we come across an incompatibility between what is capitalized in trace and the new evolved PLM. The guidance and recommendation based on last PLM before changing become incoherent with the new PLM configuration requirements. What planifications we have to think about? What is the degree of dependence between the structure of logs in face of evolution, what artefacts can be reused in spite of new changes? In this context another promising question arises: what dependency and correlations exists between the configuration process and the PLM? What is the dependence degree between a configuration process and the PLM?

On the other side, a potential problem of ConfiLog is the "cold start" phenomenon because it is evolving an automatic extraction of traces. In the beginning, our approach cannot draw any inferences for users or items about which it has not yet gathered sufficient information. Indeed, the approach always starts with an absence of background. There are no logs of activities from which we will found relevant configuration guidelines and recommendations. ConfiLog is trace-dependent, since we extract traces we are reinforcing its performance. One of the effective solutions of the "cold start" problem is to adopt machine learning techniques that can improve the performance of ConfiLog by predicting the usefulness of configuration solutions.

For analyzing and eliciting user goals when they are configuring a PL, several approaches may be exploited. We tried to think about the representative ones to improve the ConfiLog feedbacks. The timing and the manner of recommendation can take into account the configuration context and the events order. Focusing on the an evenementiel approach synchronized with a context approach seems promising. We judge that goal models ie. KAOS (Knowledge Acquisition and AutOmated Specification) proposed by [Dardenne et al. 1993] may lead to relevant solutions. Under KAOS, we handle with a conceptual elicitation and modeling of functional and nonfunctional requirements, acquisition strategies to requirements modeling and an automated assistant of the processes.

Exploring the same context of prediction and anticipation of the user goals and intentions, we can also adopt the Bayesian networks; Especially as we have goals that depend on situations. The use of a markov approach seems also promising when we are dealing with stochastic processes : the predictions depend on the current state of the decisions.

Other future directions including the validation of the performance, the accuracy, and the scalability of the proposed approach are needed.

VI.

CONCLUSION Process mining has proved a powerful way for improving many processes in many domains, such as education, catering, airports, pharmacy, etc. Our proposal is mining previous PL configurations and listen to users actions when they are configuring a product from a line in order to enhance their future configuration processes. We believe that our approach is original as it is an explicit attempt to marry the process mining and the PL engineering contexts. We are convinced also that our approach framework and process is generic and can be applied in other context to support complex business processes. However, some further work is required in order to extend this proposal. Future work will focus on the events logs extraction and propose other informations that have to be captured in the same time, analysis must be based on a larger sample of configurations and from different user profiles. We are convinced that if we develop our own logs extractor the mining task will be more profitable and promising. A next step is to translate the process mining outputs into referral recommendation techniques that guide users and provide them a better experience. Recommendation guidelines have to be automatized according to the process mining analysis.

Figure 1 -

 1 Figure 1-Process mining overview[START_REF] Aalst | Process Mining: Discovery, Conformance and Enhancement of Business Processes[END_REF]

  type of the configured feature (Template model, mandatory, grouped or optional)FeatureValueA6: The value that the user assign for the given feature? (1 for selected feature, 0 for rejected feature)FeatureDecisionType A7: The type of configuration decision (manual or propagated: Automated follows other choices)FeatureDecisionStep A8: The order of the decision in the configuration Timestamp A9 : The date and time of the decision

Goal 3 : 4 :

 34 Maximize the flexibility and efficiency of configuration alternatives to the lost users from the process miner viewpoint Goal Provide a maximum of personalization of configuration suggestions from the process miner viewpoint Goal 5: Provide recommendations of configuration suggestions from the company interests viewpoint

Figure 4 .

 4 Figure 4. General flowchart for the ConfiLog process

Figure 5 .

 5 Figure 5. Adapted version of the website Feature Model [Mendonca et al., 2008]

Figure 6 -

 6 Figure 6-Map model of the frequent decisions made during configurations of the Website PL

  their users Processes with Id Config: 29 and 39 Users : Henri + Colette ----------------------------------Processes with Id Config: 12 and 38 Users : Ines + Stephane ----------------------------------Processes with Id Config: 20 and 26 Users : Pascal + Ines -

Table 2 : GQM model 1 for "Provide user-friendliness of the configuration task from the process miner viewpoint" goal Goal 1 Purpose Provide Issue user-friendliness of Object the configuration task ViewPoin t from the process miner viewpoint

 2 What are the processes that have to be interrupted by the system?

	Questions / Metrics	Q1.1. What are the not suitable processes that lead to abandonment?
		M1.1.1. Median number of user interaction in a configuration
		M1.1.2. Number of undo actions in a configuration
		M1.1.3. Provide/not a final valid product
		Q1.2. What are the processes that include fashionable products?
		M1.2.1. Context information: Optimized/not optimized fashion interval date
		M1.2.2. Similarity measurements between configurations.
		M1.2.3. Provide/not a final valid product

Q1.3. What are the processes that include the bestseller products? M1.3.1. Similarity measurements between configurations M1.3.2. Number of mostly selected feature M1.3.3. Provide/not a final valid product Q1.4. What are the processes that include the worst-seller products? M1.4.1. Similarity measurements between configurations M1.4.2. Number of rarely selected features M1.4.3. Provide/not a final valid product Q1.5. What are the processes that include full option products? M1.5.1. Number of selected features in x configuration M1.5.2. Number of undo actions in x configuration M1.5.3. Provide/not a final valid product Q1.6. What are the processes that include minimum option products? M1.6.1. Number of rejected features in x configuration M1.6.2. Number of undo actions in x configuration M1.6.3. Provide/not a final valid product Q1.7.

Table 3 : GQM model 2 for "Reduce the required time to reach a valid configuration from the process miner viewpoint" goal

 3

	Goal 2	Purpose	Reduce
		Issue	the required time to reach
		Object	a valid configuration
		ViewPoin	from the process miner viewpoint
		t
	Questions / Metrics

Q2.1. What are the features that should be configured first? M2.1.1. Number of processes that begin with a given feature M2.1.2. Number of mostly selected features M2.1.3. Provide/not a final valid product Q2.2. What is the average number of steps and/ or required time to execute a process? M2.2.1. Number of user interactions occurred while configuring a product. M2.2.2. Time taken to complete n configuration steps M2.2.3. Provide/not a final valid product Q2.3. What is the ideal process order that minimize the solution charge? M2.3.1. Number of user interactions occurred while configuring a product. M2.3.2. Time taken to complete n configuration steps M2.3.3. Provide/not a final valid product Q2.4. What are the processes that take a lot of time? M2.4.1. Time taken to complete n configuration steps M2.4.2. Tolerance in the face of the configuration duration average Q2.5. What are the processes that were executed quickly? M2.5.1. Robot/Real user Configuration M2.5.2. Time taken to complete n configuration steps

Table 4 : GQM model 3 for "Maximize the flexibility and efficiency of configuration alternatives to the lost users from the process miner viewpoint" goal Goal 3 Purpose Maximize Issue the flexibility and efficiency of Object configuration alternatives to lost users ViewPoin t from the process miner viewpoint Questions / Metrics

 4

	Q3.1. How to know if the user is lost in a configuration process?
	M3.1.1. Time taken to configure a given feature
	M3.1.2. Number of undo actions in x configuration
	Q3.2. What are the features that cause backtracks during the configuration
	process?

M3.2.1. Successful/Unsuccessful undo actions in x configuration M3.2.2. Manual/automatic value assignment of x feature M3.2.3. Number of implied features in an undo actions Q3.3. What are the processes that are executed with a maximum number of manual (user) choices? M3.3.1. Number of Manual/automatic choices in x configuration M3.3.2. Number of undo actions in x configuration Q3.4. What are the processes that are executed with a maximum number of automatic (propagated) choices? M3.4.1. Number of Manual/automatic choices in x configuration M3.4.2. Number of undo actions in x configuration Q3.5. What are the doubtful paths that lead to interrupt the process? M3.5.1. The average of transitions frequencies between feature M3.5.2. Number of undo actions in x configuration M3.5.3. Provide/not a final valid product Q3.6. What are the safest paths that lead to a valid product? M3.6.1. The average of transitions frequencies between feature M3.6.2. Number of undo actions in x configuration M3.6.3. Provide/not a final valid product

Table 5 : GQM model 4 for "Provide a maximum of personalization of configuration suggestions from the process miner viewpoint" goal Goal 4 Purpose Provide Issue a maximum of personalization Object of configuration suggestions ViewPoin t from the process miner viewpoint

 5

	Questions / Metrics	Q4.1. What are the processes that were configured by serious users?
		M4.1.1. Average time taken by an x user to complete a configuration step
		M4.1.2. Number of successful tasks in given time
		M4.1.3. Number of selected/rejected features comparing to the average

Q4.2. What are the processes of a specific user based on his history? M4.2.1. Features selections and rejections of a given user M4.2.2. Similarity measurements between configurations of an x user M4.2.3. Timestamp of a given user Q4.3. What are the users that have similar configuration intentions? M4.3.1. Average of Features selections and rejections of n users M4.3.2. Similarity measurements between given configurations Q4.4. * What are the processes that include the best-seller products for each category of users (Gender, Age, Country)/ or for a specific period (for example summer holidays) M4.4.1. Similarity measurements between configurations M4.4.2. Include/not common processes executions M4.4.3. Provide/not a final valid product M4.4.4. Context informations: Measurements of differences and similarities between users M4.4.5. Context informations : Given period of filter * : A question that need domain informations GQM Model 5 : Table 6 describes the GQM model for the following goal: "Provide recommendations of configuration suggestions from the company interests viewpoint".

Table 6 : GQM model 5 for "Provide recommendations of configuration suggestions from the company interests viewpoint" goal Goal 5 Purpose Provide Issue Recommendations Object of configuration suggestions ViewPoin t from the company interests viewpoint Question / Metrics

 6

	Q5.	* What are the processes that lead to decrease the product cost and/or to
	increase the profitability for the company and/or to sell as much stock and/or
	to minimize the human resources cost
	M5.1. Similarity measurements between configurations.
	M5.2. Include/not common processes executions
	M5.3. Provide/not a final valid product
	M5.4. Context informations: Development cost of each feature
	M5.5. Context informations : Existent Stock of each feature
	M5.6. Context informations : Human resource needs of each feature

* : A question that need domain informations

  .1. Context information: Optimized/not optimized fashion interval date, M1.2.2.

	between n configurations.
	Similarity measurements between configurations, M1.2.3. Provide/not a final valid product,
	M1.3.1. Similarity measurements between configurations, M1.3.2. Number of mostly selected
	feature, M1.3.3. Provide/not a final valid product, M1.4.1. Similarity measurements between
	configurations, M1.4.2. Number of rarely selected features, M1.4.3. Provide/not a final valid
	product, M1.5.1. Number of selected features in x configuration, M1.5.2. Number of undo actions
	in x configuration, M1.5.3. Provide/not a final valid product, M1.6.1. Number of rejected features
	in x configuration, M1.6.2. Number of undo actions in x configuration, M1.6.3. Provide/not a final
	valid product, M4.2.1. Features selections and rejections of a given user, M4.2.2. Similarity
	measurements between configurations of an x user, M4.2.3. Timestamp of a given user, M4.3.1.
	Average of Features selections and rejections of n users and M4.3.2. Similarity measurements

Table 7 : GQM based on mining website line configuration processes using DISCO Goal Question Metric Attributes focus Mining Queries & comments Recommendation Results A1 A2 A3 A4 A5 A6 A7 A8 A9

 7

	G1	Q1.2 M1.2.1,	x	x	According to the
		M1.2.2,			context :
		M1.2.3
					1 /Select processes that
					have recent
					Date and time < 2
					days
					2 /Select Variant with
					Max similar processes

Q1.6 M1.6.1, M1.6.2, M1.6.3

	G4	Q4.2 M4.2.1,	x x		Select Processes
		M4.2.2,			executed by User = X
		M4.2.3
					(Sonia for example)
			x	x x x	1 /Select Processes that
					have max of
					Feature Value = 0

http://www.sei.cmu.edu/. SEI web page

https://variamos.com/home/

http://www.splot-research.org/

http://www.promtools.org/doku.php

https://fluxicon.com/disco/

video demonstration of our approach showing all tools, methods and procedures to do

process mining in the context of PL configuration. In this demonstration we used the running example (cf. section III.C) in order to guide users.

https://docs.google.com/document/d/1M1sJ6nIxOvMuy_IoKo2fj_aCFIqGZjCTBCcI7Wj6Q2M/edit?usp=sharing

https://www.youtube.com/watch?v=TdPe8Felgk8

/Select processes without Backtracks.

ACKNOWLEDGEMENTS

This work was supported by the French-Tunisian CMCU project 16G/1416 (CONFIGURE). All the authors of this paper are grateful and thank the committee members of this research program for their assistance and financial support.


Amélie Wannebroucq

Guillaume Gruntz

Jean-Moïse Suisse

Yohann Nicolas
email: yohann.nicolas@enscbp.fr

Rita Meunier-Prest

Mickaël Mateos

Thierry Toupance

Marcel Bouvet
email: marcel.bouvet@u-bourgogne.fr

New n-type

Keywords: Triphenodioxazine, Molecular materials, Heterojunctions, Conductometric transducer, Ammonia

Introduction

The wide attention that has been given to organic electronic devices over the past decades was due to their expected straightforward and low-cost processing through printing technologies as well as the possibility of designing flexible systems. In addition, new logic circuits and organic solar cells involve a suitable combination of p-type and n-type organic semiconductors. For example, a blend of poly-3-hexylthiophene and PCBM (a C60 derivative) used as p-type and n-type materials respectively is well known for its photovoltaic properties [START_REF] Peet | Plastic" solar cells: Self-assembly of bulk heterojunction nanomaterials by spontaneous phase separation[END_REF]. However, the number of n-type organic materials is so far rather limited compared to hole (p-type) transporting materials. Thus, many efforts are currently devoted to the development of stable n-type organic semiconductors and to their integrations into n-type organic fieldeffect transistors and logic circuits [START_REF] Crone | Large-scale complementary integrated circuits based on organic transistors[END_REF][START_REF] Klauk | Flexible organic complementary circuits[END_REF][START_REF] Zaumseil | Electron and ambipolar transport in organic field-effect transistors[END_REF][START_REF] Usta | n-Channel semiconductor materials design for organic complementary circuits[END_REF].

A few years ago, Bouvet et al. introduced a new type of heterojunction [START_REF] Parra | Semiconductor transducer and its use in a sensor for detecting electron-donor or electron-acceptor species[END_REF], which is built from a rather poor conducting molecular material as sublayer covered by a highly conductive molecular material, the lutetium bisphthalocyanine (LuPc2, Scheme 1). Since the resistive sublayer is much thinner than the interelectrode distance, under polarization, charges have to move across the interface between the two molecular materials in order to take the most conductive pathway when applying a bias voltage, as shown in Fig. 1. This device has been named MSDI, which stands for Molecular Semiconductor -Doped Insulator heterojunction [START_REF] Parra | Molecular semiconductor-doped insulator (MSDI) heterojunctions: an alternative transducer for gas chemosensing[END_REF]. It is worth underlying that a MSDI is neither a p-n junction nor a transistor, and has its own current-voltage characteristics. Owing to its particularly low energy gap, ca. 0.5 eV [START_REF] André | Electrical and magnetic properties of thin films and single crystals of bis(phthalocyaninato)lutetium[END_REF][START_REF] Bouvet | Electrical properties of rare earth bisphthalocyanine and bisnaphthalocyanine complexes[END_REF],

resulting from its radical nature, the lutetium bisphthalocyanine exhibits a high density of positive and negative charge carriers, and, therefore, shows ambipolar conducting properties. Thus, LuPc2 is the first molecular semiconductor to have led to a n-channel in an OFET [START_REF] Guillaud | Field-effect transistors based on intrinsic molecular semiconductors[END_REF]. This architecture involving LuPc2 exhibits very specific response when exposed to gases. Using pure n-type sublayers, such as metalloperfluorophthalocyanines, M(F16Pc), the response to ammonia is positive whereas the electrical current decreases for a LuPc2 resistor. On the other hand, with a pure p-type sublayer, e.g. pentacene, oligothiophene or a nonsubstituted phthalocyanine [START_REF] Parra | Molecular semiconductor-doped insulator (MSDI) heterojunctions: an alternative transducer for gas chemosensing[END_REF][START_REF] Bouvet | Molecular semiconductor-doped insulator (MSDI) heterojunctions: Oligothiophene/bisphtalocyanine (LuPc2) and perylene/bisphthalocyanine as new structures for gas sensing[END_REF], ammonia induces a decrease in the current through the MSDI. An important point about MSDIs is that the only material that can interact with the outer atmosphere is the top layer.

Nevertheless, we showed that the transport properties are dominated not only by the energy barrier at the interface between the sublayer and the LuPc2 top layer, but also by the Schottky contact between the sublayer and the electrodes [START_REF] Bouvet | Revisiting the electronic properties of Molecular Semiconductor -Doped Insulator (MSDI) heterojunctions through impedance and chemosensing studies[END_REF]. These energy barriers are higher with M(F16Pc) than with p-type sublayers. Beside perfluorinated phthalocyanines [START_REF] Bao | New air-stable n-channel organic thin film transistors[END_REF], only few molecular materials exhibit an n-type behavior in air. The most popular are naphthalene diimide and perylene diimide derivatives [START_REF] Zaumseil | Electron and ambipolar transport in organic field-effect transistors[END_REF][START_REF] Katz | A soluble and air-stable organic semiconductor with high electron mobility[END_REF][START_REF] Katz | Naphthalenetetracarboxylic diimidebased n-channel transistor semiconductors: Structural variation and thiolenhanced gold contacts[END_REF][START_REF] Jones | Tuning orbital energetics in arylene diimide semiconductors. Materials design for ambient stability of n-type charge transport[END_REF], C60 [START_REF] Haddon | C60 thin film transistors[END_REF][START_REF] Li | High-mobility fieldeffect transistors from large-area Ssolution-grown aligned C60 single crystals[END_REF] and fluorinated oligothiophenes [START_REF] Usta | n-Channel semiconductor materials design for organic complementary circuits[END_REF]. On the other hand, triphenodioxazine (TPDO) is a well-known -conjugated core found in different dyes or pigments [START_REF] Renfrew | Reactive dyes for cellulose: Replacement of anthraquinone blues by triphenodioxazines[END_REF] that has recently found new promising development in the field of organic solar cells [START_REF] Guo | A fluorene-fused triphenodioxazine (FTPDO) based polymer with remarkable thermal stability and significantly enhanced charge transport performance in air[END_REF], dye-sensitized solar cells [START_REF] Nicolas | New synthetic routes towards soluble and dissymmetric triphenodioxazine dyes designed for dye-sensitized solar cells[END_REF], luminescent devices [START_REF] Tanaka | Highly dichroic and luminescent triphenodioxazine dyes[END_REF] and organic field-effect transistors [START_REF] Di | Trifluoromethyltriphenodioxazine: Air-stable and high-performance n-type semiconductor[END_REF][START_REF] Nicolas | TIPStriphenodioxazine versus TIPS-pentacene: Enhanced electron mobility for n-type organic field-effect transistors[END_REF]. For instance, triphenodioxazines (TPDOs) bearing triisopropylsilylethynyl (TIPS) groups were introduced as new soluble n-type materials [START_REF] Nicolas | New synthetic routes towards soluble and dissymmetric triphenodioxazine dyes designed for dye-sensitized solar cells[END_REF][START_REF] Nicolas | TIPStriphenodioxazine versus TIPS-pentacene: Enhanced electron mobility for n-type organic field-effect transistors[END_REF] (scheme 1). More interestingly, tetracyano-TIPS-TPDO proved to be an efficient material for liquid-processed air-stable n-type OFET [START_REF] Gruntz | Nitrile substitution effect on triphenodioxazine-based materials for liquid-processed airstable n-type organic field effect transistors[END_REF][START_REF] Jung | A TIPS-TPDO-tetraCN-based n-type organic field-effect transistor with a crosslinked PMMA polymer gate dielectric[END_REF].

In the present paper, we report on the behavior of MSDI heterojunctions combining the tetracyano-TIPS-TPDO and the lutetium bisphthalocyanine LuPc2 (Scheme 1). Their current-voltage characteristics are compared to those of resistors prepared from one of these two molecular materials. Their response to ammonia is studied in the 10-100 ppm range, in synthetic air.

Experimental

Chemicals

Previously reported procedures were followed to prepare LuPc2 [START_REF] Kirin | Formation of unusual phthalocyanines of the rare-earth elements[END_REF][START_REF] Clarisse | Synthesis and characterization of some lanthanide phthalocyanines[END_REF] and tetracyano-TIPS-TPDO [START_REF] Gruntz | Nitrile substitution effect on triphenodioxazine-based materials for liquid-processed airstable n-type organic field effect transistors[END_REF]. Chloroform and chlorobenzene are used as solvent for spin coating. Polystyrene (M = 280 000) and other chemicals are purchased from (Sigma-Aldrich) and used as received.

Characterizations

The UV-visible absorption spectra were registered using a Varian UV-vis spectrophotometer Cary 50, between 350 nm and 850 nm. Field Desorption (FD) mass spectra were performed by the CESAMO (Bordeaux, France). The measurements were carried out on a TOF mass spectrometer AccuTOF GCv by JEOL using an FD emitter with an emitter voltage of 10 kV. One to two microliters solution of the compound was deposited on a 13 µm emitter wire. Thermogravimetric analyses were carried out on a Netzsch STA simultaneous analyzer using alumina crucibles.

Electrical and chemosensing measurements

Electrical measurements were carried out with Indium Tin Oxide (ITO) interdigitated electrodes (IDE) deposited onto a 1 x 1 cm² glass substrate and separated by 75 m.

Their thickness was 50 nm and their total length 15 cm. Thin films of molecular materials were prepared either by sublimation under secondary vacuum (ca. 10 -6 mbar) in a UNIVEX 250 thermal evaporator (Oerlikon, Germany), at a rate of ca. 0.3 Å s -1 , by heating in a temperature range of 320-350 °C for TPDO and at a rate of 1 Å s -1 , 450-500 °C for LuPc2, or by the spin coating technique. Thus, films of pure TPDO and films of TPDO mixed with 13% (w/w) of polystyrene were prepared from chloroform/chlorobenzene 9/1 solutions. The temperature of the solution was raised to 50°C before spin coating. The workbench used for the study of ammonia sensing was described previously [START_REF] Gaudillat | Bias and humidity effects on the ammonia sensing of perylene derivative/lutetium bisphthalocyanine MSDI heterojunctions[END_REF]. Ammonia gas (NH3), 1000 ppm in synthetic air, and synthetic air were used from standard cylinders, purchased from Air Liquide, France. The total flow was in the 0.5-0.55 NL min -1 range, depending on ammonia concentration, and the volume of the test chamber was 8 cm 3 . Gas sensing experiments were carried out in a dynamic way, with 4 min-long rest periods alternating with 1 min-long exposure periods. In the present study, all the exposures to NH3 were carried out in dry air and at the lab temperature (18-22 °C, unless otherwise specified).

Results and Discussion

After deposition of TPDO by spin coating, LuPc2 (50 nm) was deposited by evaporation under secondary vacuum. The UV-visible spectrum of the resulting bilayer film exhibits the main absorption bands of TPDO, between 500 and 650 nm [START_REF] Nicolas | TIPStriphenodioxazine versus TIPS-pentacene: Enhanced electron mobility for n-type organic field-effect transistors[END_REF][START_REF] Gruntz | Nitrile substitution effect on triphenodioxazine-based materials for liquid-processed airstable n-type organic field effect transistors[END_REF],

with a peak at 638 nm, and of LuPc2, with its Q band at 668 nm and the weak band at 467 nm attributed to a transition between a filled orbital and the SOMO (Fig. 2) [START_REF] Even | Optical properties of thin films of molecular semiconductors[END_REF][START_REF] Orti | Electronic structure of phthalocyanines: Theoretical investigation of the optical properties of phthalocyanine monomers, dimers, and crystals[END_REF]. The I-V characteristics, registered from -10 to +10 V, are symmetrical and slightly nonlinear, with a current of 7x10 -5 A at 10 V (Fig. 3 left). Under a constant polarization of 1 V, the device exhibits a current increase under NH3 (Fig. 3 right), as expected for a n-type MSDI. This clearly confirms the n-nature of the majority charge carriers in the TPDO sublayer. Indeed, MSDIs built with a p-type sublayer lead to a negative response to donating species like NH3 [START_REF] Parra | Molecular semiconductor-doped insulator (MSDI) heterojunctions: an alternative transducer for gas chemosensing[END_REF]. However, the response is rather poor. With exposure/recovery cycles of 1 min/4 min, the relative response to 90 ppm NH3 after a 1 min-long exposure period, defined as (I-I0)/I0, where I and I0 are the current values at the end and at the beginning of exposure periods, respectively, is only 0.2 %. More important is the evolution of the transport properties of the device. After a series of expositions to NH3/rest under synthetic air cycles, the I-V characteristics become linear and NH3 induces a current decrease, indicating a modification of the electronic transport through the device, the origin of which remains unknown. This behavior was observed for samples prepared with pure TPDO and also for these from TPDO solutions containing 13 % (w/w) of polystyrene. This is the reason why devices were prepared by vacuum evaporation of both components, TPDO and LuPc2, with thicknesses of 50 nm each. They showed a significantly different behavior. In particular, their electrical characteristics and their chemosensing responses remain stable over time. The I-V characteristics are symmetrical and nonlinear, with a current of 1.35x10 2 A at 10 V (Fig. 4). When exposed to NH3, the current increases sharply during the first minute, up to a plateau, and a full reversibility is observed under synthetic air, as depicted from 15 min/1 h exposure/rest cycles (Fig. 5). Again, this positive response to NH3 is the signature of a n-type MSDI. When submitted to repeated exposure/recovery cycles with exposure periods of 1 min separated by a 4 min-long recovery period under a synthetic air flow, the device shows a good reversibility, with a slight drift. The response depends on the NH3 concentration, as depicted from Fig. 6, in the 30-90 ppm range. The relative response is about 11% at 30 ppm, which is as much as that of the most sensitive n-MSDI reported so far, namely the MSDIs prepared with perfluorophthalocyanine complexes as a sublayer. In comparison, a resistor made of a vacuum deposited TPDO film in the same conditions exhibits a much lower current, about 10 -9 A under 10 V. This increases under ammonia, by 6% after one minute at 50 ppm. However, the response changes only slightly between 25 ppm and 90 ppm (Fig. 7). Again, this curve shows the n-type character of the material. The important drift of the current with and without ammonia limits highly the interest of such a resistor. This drift was also observed when the resistor was exposed to 90 ppm of NH3 during 15 min/60 min exposure/recovery cycles (Fig. S1). The current drift is so high, with and without ammonia, that no plateau is visible. In turn, it highlights the advantages of the MSDI heterojunctions compared to resistors. The UV-visible absorption spectra of vacuum evaporated TPDO films with and without LuPc2 as a top layer (Fig. 8) are different from those expected for TPDO and obtained by spin coating (Fig. 2) [START_REF] Nicolas | TIPStriphenodioxazine versus TIPS-pentacene: Enhanced electron mobility for n-type organic field-effect transistors[END_REF][START_REF] Gruntz | Nitrile substitution effect on triphenodioxazine-based materials for liquid-processed airstable n-type organic field effect transistors[END_REF]. In particular, additional bands appear in the vacuum evaporated TPDO films, which are centered at 459, 485 and 581 nm, while the max of spin coated TPDO films lies at 638 nm. In the corresponding MSDIs, additionally to the Q band of LuPc2 (668 nm), another feature is the absorption at 460 nm, related to the radical nature of LuPc2 [START_REF] Even | Optical properties of thin films of molecular semiconductors[END_REF][START_REF] Orti | Electronic structure of phthalocyanines: Theoretical investigation of the optical properties of phthalocyanine monomers, dimers, and crystals[END_REF], that becomes equal to this at 479 nm, owing to the absorption of both materials at this wavelength. The broad band centered at 596 nm results also from both materials, whereas the band at 638 nm (Fig. 2) corresponding to TPDO disappeared. These differences with the expected spectra are not due to a degradation of the TPDO molecule since it starts above 350 °C as depicted from thermogravimetric analyses. In addition, the mass spectrum of the evaporated derivative shows a main peak at an exact mass of 748.3365, which can be assigned to the diprotonated molecule, H2TPDO (Fig. 9). The mass difference with the chemical formula, C44H48N6O2Si2, is of 1.59 ppm, which is a very small value sign of an unambiguous assignment.

For the native TPDO, the exact mass is 746.3193 (M), and the first fragmentation corresponds to the loss of one of the propyl groups (M-43). This new formula (H2TPDO) corresponds to a reduction of the starting TPDO by two electrons and two protons, analogous to the reduction of quinones into dihydroquinones [START_REF] Lemmer | Proton coupled electron transfer of ubiquinone Q2 incorporated in a self-assembled monolayer[END_REF]. This reduction can occur at the quinonediimine level, by its transformation into its diamino analogue (Fig 9) [START_REF] Bolognese | Formation of dihydrotriphenodioxazines and dihydroisotriphenodioxazines by acidic treatment of some substituted 3H-phenoxazin-3-ones; isolation and characterization. A new perspective in the chemistry of ommochromes[END_REF]. Another characteristic of the reduced derivative is its thin vibration band at 3372 cm -1 , attributed undoubtedly to a stretching vibration of a N-H bond, which is absent in TPDO. The study of the reduction of TPDO in solution will be reported elsewhere [34]. While the native quinonediimine form of TPDO is stable in air, the reduced form, H2TPDO, is produced during heating under secondary vacuum (P = 10 -6 mbar). The only way to explain the existence of this reduced form in the solid state is to take into account the intermolecular interactions, mainly  interactions and Hbonds, since the reduced form owns two additional H atoms, bear by nitrogen atoms, capable to form H-bonds with neighboring molecules. Resistors based on substituted bisphthalocyanine complexes with a good sensitivity to NH3 have been reported [START_REF] Chen | Tuning the semiconducting nature of bis(phthalocyaninato) holmium complexes via peripheral substituents[END_REF], with a positive or negative response, depending on the peripheral substitution of the macrocycles. However, clearly, as for the resistor made from TPDO derivatives in the present study, there is an important drift of the current over time. In an example [START_REF] Kong | High sensitive ambipolar response towards oxidizing NO2 and reducing NH3 based on bis(phthalocyaninato) europium semiconductors[END_REF] a response of ca. 2 % at 10 ppm NH3 was reported but the current-time curve was noisy. We want to point out that the current response of MSDIs does not suffer this drawback. We previously underlined the advantage of n-type MSDIs for chemosensing (see for examples [START_REF] Parra | Semiconductor transducer and its use in a sensor for detecting electron-donor or electron-acceptor species[END_REF] and [START_REF] Bouvet | Molecular semiconductor-doped insulator (MSDI) heterojunctions: Oligothiophene/bisphtalocyanine (LuPc2) and perylene/bisphthalocyanine as new structures for gas sensing[END_REF]). In particular, the high sensitivity of the radical LuPc2 top layer is kept, but with a higher stability of the response over time compared to resistors made from one of the components. Moreover, with a n-type sublayer, the response to NH3 is positive (current increase), which is always an advantage compared to a negative response. n-Type semiconductors offer this advantage, but due to their poor long term stability in air, their use in resistors is de facto very limited. Their use in MSDI heterojunctions offers the opportunity to keep the advantage of n-type materials to detect electrodonating gases without their instability in air. A key difference between conventional resistors and n-MSDIs is that the responses to ammonia and humidity are inverted in n-MSDIs whereas these species act both as donating species in resistors. The effect of water was interpreted as an effect of trapping of positive charge carriers by water molecules leading to a decrease of the energy barrier in MSDIs, then an increase of the current going through the device. The effect of ammonia is the neutralization of positive charge carriers by ammonia leading to an increase in minority (negative) charge carriers, which govern the transport properties of n-type MSDIs [START_REF] Bouvet | Revisiting the electronic properties of Molecular Semiconductor -Doped Insulator (MSDI) heterojunctions through impedance and chemosensing studies[END_REF].

The response of the vacuum evaporated TPDO/LuPc2 MSDI towards NH3 was study under humidity, in the range 10-70 % (Fig. 10). The humidity decrease induces a slight drift of the current, by 4.5 % from 70 % rh to 10 % rh. This variation is lower than the response to 30 ppm NH3. The response to NH3 increases with the rh value, from 9.1 % to 14.8 % at 30 ppm NH3 when the rh increases from 10 % to 70 %. Up to now the reported n-type MSDI heterojunctions were limited to perylene derivatives and perfluorophthalocyanines. We already demonstrated a long-term stability of the chemosensing behavior of n-MSDIs with a perfluorophthalocyanine complex as a sublayer, namely several years [START_REF] Bouvet | Revisiting the electronic properties of Molecular Semiconductor -Doped Insulator (MSDI) heterojunctions through impedance and chemosensing studies[END_REF]. On the contrary, perylene derivatives that exhibit rather nice responses to NH3 lead to sensors with a rather poor stability over time [START_REF] Gaudillat | Bias and humidity effects on the ammonia sensing of perylene derivative/lutetium bisphthalocyanine MSDI heterojunctions[END_REF].

The present MSDIs have been submitted to 30 ppm NH3 during twenty exposure/recovery cycles (1 min/4 min) at 50 % rh (Fig. 11). A small drift appears for the current with and without NH3 and the relative response remains constant over all the experiment, at 14.9 % ±0.1. So, with this present work we showed the interest of TPDO derivatives as a component in such conductometric sensors. It opens the way for new applications for this family of n-type materials already used in organic electronics, but in OFETs and in photovoltaic cells.

Conclusion

Sensing ammonia with an MSDI involving LuPc2 and tetracyano-TPDO confirms the n-type character of the conductivity of TPDO. The reduced form of the tetracyano-TPDO obtained during its vacuum evaporation revealed to be also a n-type material, with a better stability of its electrical and chemosensing properties when associated to LuPc2 in MSDI devices. The positive response to NH3 is high enough for its detection in the range 10-100 ppm, which is interesting for many applications. Thus, in chemical industry, the threshold of 50 ppm is generally considered, and the European air quality labor legislation sets the daily exposure limit to 20 ppm for NH3 [START_REF] Timmer | Ammonia sensors and their applications-a review[END_REF]. It also confirms, if needed, that the MSDI device is a versatile conductometric transducer, capable of accommodating many molecular materials, and of operating at room temperature. TPDO derivatives favorably compete previous materials leading to n-type MSDIs, namely perfluorophthalocyanines and perylene derivatives.

Scheme 1 .

 1 Scheme 1. Top view of LuPc2 along its C4 axis (left) and schematic view of the tetracyano-TIPS-TPDO (right).

Figure 1 .

 1 Figure 1. Schematic view of a Molecular Semiconductor -Doped Insulator (MSDI) heterojunction (left) compared to a resistor (right); the arrows indicate the main channel for charge carriers.

Figure 2 .

 2 Figure 2. UV-vis absorption spectrum of a spin coated TPDO/LuPc2 (black line) bilayer deposited on a glass substrate compared to the spectra of spin coated TPDO (red, dotted line) and evaporated LuPc2 (green, dashed line) films.

Figure 3 .

 3 Figure 3. Typical I-V characteristic of a spin coated TPDO/LuPc2 MSDI (left) and its response to 90 ppm of NH3 (exposure duration: 15 min) in dry synthetic air when polarized at 1 V (right).

Figure 4 .

 4 Figure 4. Typical I-V characteristic of a vacuum evaporated TPDO/LuPc2 MSDI.

Figure 5 .

 5 Figure 5. Current variation as a function of time of a vacuum deposited TPDO/LuPc2 MSDI, polarized at 1 V, exposed to 90 ppm NH3 for 15 min-long periods separated by 1 h-long rest periods in dry synthetic air.

Figure 6 .

 6 Figure 6. Response of a vacuum deposited TPDO/LuPc2 MSDI to NH3 in the range 30-90 ppm), in dry atmosphere, during exposure/recovery cycles (1 min/4 min), polarized at 1 V.

Figure 7 .

 7 Figure 7. Current variation as a function of time of a vacuum deposited TPDO resistor, polarized at 10 V, exposed to ammonia in dry synthetic air at different concentrations for 1 min-long periods spaced by a 4 min-long rest period under dry synthetic air. The ammonia

Figure 8 .

 8 Figure 8. Absorption spectra of a vacuum evaporated TPDO/LuPc2 MSDI (50 nm/50 nm) (top, black line) and of a vacuum evaporated TPDO resistor (50 nm, red dotted line)

Figure 9 .

 9 Figure 9. Mass spectra of the triphenodioxazine derivative (bottom) and its reduced form (top) obtained in positive mode. Chemical structures (only the two additional H atoms are shown in the reduced form), radical lost fragment, exact mass and differences with theoretical mass are inserted.

Figure 10 .

 10 Figure 10. Response of a vacuum evaporated TPDO/LuPc2 MSDI (solid line) to NH3 (90, 60 and 30 ppm, dotted line), with relative humidity in the range 10-70 % (dashed line), during exposure/recovery cycles (1 min/4 min), polarized at 2 V. The temperature was 23.5 °C.

Figure 11 .

 11 Figure 11. Response of a vacuum evaporated TPDO/LuPc2 MSDI (solid line) to 30 ppm NH3 (dotted line) at a relative humidity of 50 %, during twenty exposure/recovery cycles (1 min/4 min), polarized at 2 V. The temperature was 23.5 °C.

Acknowledgements

The authors acknowledge the Agence Nationale de la Recherche for funding through the projects CAP-BTX ANR-BLAN-2010-917-02, FMOCSOLE ANR-BLAN-2010-938-01 and OUTSMART ANR-2015-CE39-0004-03 and the MENESR for a PhD grant (A. W.). Financial support from the European Union (FEDER) and the Conseil Régional de Bourgogne through the FABER and the PARI SMT 08 and CDEA programs is gratefully acknowledged. We would like to thank the European Union for funding through the COST action TD1105 EuNetAir. The University of Bordeaux (G.G. PhD support), the CNRS and the institute of polytechnique of Bordeaux are acknowledged for financial support.