Stéphane Bellini

THE OVER-FIFTIES: VICTIMS OF ANTHROPOLOGICAL SCREENING?

France has one of the lowest employment rates for the over-fifties in Europe. Several surveys have confirmed that strategies for managing the over-fifties in French companies share a common characteristic: the search for ways to remove them. In this paper, we shall be investigating the reasoning behind the ousting of the over-fifties. We shall look closely at the explanations that purportedly show that these decisions are objectively based and apparently impartial, before going on to investigate just how rational these practices are. Our desire is to show that such widespread practices are based on widely shared schemes of thought. We use anthropological screening to refer to the process of using an implicit model of the individual at work as a criterion for selection and promotion. While formerly anthropomorphic 1 in nature, this screening now centres on types of skills, relationships and performance at work, the ability to fit into an organisation and psychological predispositions. How a person behaves matters as much as what he or she does.

INTRODUCTION

In contrast to restructuring operations that take place in a media frenzy as they focus on one particular time and place, the departure of the oldest employees is often greeted with silence or even approval when it takes the form of early retirements. The reasons for the departure of staff are obscured when it happens in a thousand different places. Nevertheless, although they may not happen at one particular time and place, these silent reorganisations share one common element: they involve older employees: the "over-fifties", who, it is widely believed, are incapable of adapting to modern demands (see [START_REF] Volkoff | Vieillir au travail: un atout ou une préoccupation ?[END_REF][START_REF] Guillemard | L'âge de l'emploi. Les sociétés à l'épreuve du vieillissement[END_REF].

The results of this are well known: France has one of the lowest employment rates for the over-fifties in Europe2 . Several surveys have confirmed that strategies for managing the overfifties in French companies share a common characteristic: the search for ways to remove them. Only a minority of companies endeavour to improve working conditions, implement end of career management strategies and transfer the experience of older employees [START_REF] Topiol | Les entreprises se préoccupent peu du vieillissement démographique[END_REF], 2004;[START_REF] Falcoz | Parcours professionnel et employabilité des 50 ans et plus: représentations, pratiques de gestion et enjeux socio-économiques[END_REF]. On the other hand, policies targeting the number of overfifties in work are more common, i.e. general measures that focus on one medium-term goal.

More often than not, these involve organising early retirements or restructuring operations designed to exclude the oldest employees [START_REF] Bellini | Une typologie de pratiques de gestion des seniors[END_REF]. The departure of the over-fifties thus results in micro-restructuring operations at the workshop or office level. This type of practice conforms to the French tradition of ousting older employees [START_REF] Poilpot-Rocaboy | Performance de la gestion des emplois : éviction ou rétention des salariés vieillissants ?[END_REF], whose pensioning off has traditionally been decided by the different actors in the organisation: namely, the HR manager, staff representatives and the employees themselves.

There is nothing new in this low level of employment, or in the absence of observable practices. What has changed is the amount of media attention given to the over-fifties since pension reforms and the Council of Europe highlighted them as a French anomaly in need of rectification. At present, the use of early retirement is starting to be perceived as less of a social shock-absorber and more of an economic anomaly.

In this paper, we shall be investigating the reasoning behind the ousting of the over-fifties and why they are a priority target for job cuts3 . First, we shall look closely at the explanations that purportedly show that these decisions are objectively based and apparently impartial, before going on to investigate just how rational these practices are. Our desire is to show that such widespread practices are based on widely shared schemes of thought. The stakes are high for older employees because in no way does the disappearance of age policies mean an end to the ousting of the over-fifties; it simply means that this operation will no longer be paid for by the State. Without a reorientation of the schemes of thought concerning the over-fifties population, the costs of the lay-offs will be borne by the companies themselves or by national solidarity organisations (unemployment insurance, health insurance or income support depending on the situation).

Our analysis is based upon three complementary investigative approaches, all of which share the use of qualitative methodologies designed to obtain contextualised information and understand the reasoning used to justify these practices. At different levels, each of the following surveys has helped to improve our understanding of the reasons for the ousting of the over-fifties:

-An initial survey carried out in around fifteen medium and large companies (120 to 160 employees) led to the identification of management practices used for employees over the age of fifty. This was performed at the request of ARACT Poitou-Charentes 4 , which is itself involved in the A tout Age (At Any Age) European project. On this occasion, we carried out in-depth interviews with HR managers and combined this information with interviews with occupational doctors and/or nurses where possible, along with observations from a tour of the company itself. In addition to identifying management practices, this survey helped us to understand the reasons for their implementation or absence -An awareness-raising mission concerning the GPEC5 approach in around 20 small companies in the Poitiers urban area (with fewer than 20 employees). In the framework of this action, we carried out a series of three interviews with the heads of each company. This strategy gave us an insight into how these managers accounted for their population of over-fifties when planning for the future -A participant observation on VAE (Validation of Acquired Experience) assessment panels. The aim was to understand the processes involved in the evaluation of professional experience. In the first two surveys, this quality was perceived to be the key asset of the over-fifties, even when the intention was to lay them off. By observing how experience is assessed in proceedings that are dedicated to this issue, we learnt more about a process which is designed to increase its standing.

THE EXPRESSION OF A FORM OF ANTHROPOLOGICAL SCREENING

Certain arguments are used to point to the weaknesses of the over-fifties at work: physical problems, outdated technical skills, lost motivation, resistance to change in general and ICT in particular, being more costly to employ than young people, etc. These arguments are used to justify the ousting of the over-fifties. However, each of these points needs to be discussed, qualified and put into perspective, primarily because any age category, whether young or old, encompasses a population which is heterogeneous in many respects. More than reflecting an accurate view of reality, the arguments which we shall discuss later on in this paper seem to reveal the existence of an implicit norm to which workers are supposed to conform and that the over-fifties are seen in an unfavourable light when compared to this norm. On the grounds of their supposed failure to conform to a model, certain individuals within a population are excluded from employment or looked upon in a poor light. This applies to the over-fifties but also to young people, women and anyone who is "different". The very terminology for these groups is based on the existence of this model because, to state the obvious, one is only different in relation to a given model.

The phenomena of excessive generalisation, the homogenisation of a heterogeneous population (Duyck and Guérin, 2005) and the attribution of specific traits to everyone within a particular group without distinction does not just happen to the over-fifties in the world of work. This is also a problem that affects the over-fifties in other walks of life. Society as a whole portrays the over 50s as being completely different to young people, who are lauded for their modern technical skills and their supposed capabilities [START_REF] Guillemard | L'âge de l'emploi. Les sociétés à l'épreuve du vieillissement[END_REF]. One can see it in action: the typecasting of the over-fifties both creates and reflects an image of employees who do not conform to an implicit reference model that is used as a gauge.

We use anthropological screening to refer to the process of using an implicit model of the individual at work as a criterion for selection and promotion. While formerly anthropomorphic 6 in nature, this screening now centres on types of skills, relationships and performance at work, the ability to fit into an organisation and psychological predispositions.

How a person behaves matters as much as what he or she does. This screening may be implicit, but its consequences, on the other hand, are very real. Thus, on the subject we are dealing with here, it results in the exclusion of the oldest employees from training, a preference for other staff to handle important projects and the ousting of older staff from organisations7 . Furthermore, according to the principle of self-fulfilling predictions, a negative attitude towards older employees creates a diagnosis that serves as a justification: an absence of training, reaching a dead end in one's career and being given unrewarding projects results in the creation of wait-and-see behaviours and a reluctance to look to the future… finally justifying the typecasting of older employees who end up internalising such images of themselves.

The physiological argument

The physiological argument is frequently used to justify encouraging older employees to leave their posts. The latter are indeed more prone to occupational illnesses than other age groups (Vauclin and Thamain, 2005) and a number of HR managers mention the difficulty of reassigning employees who have become unfit for work. This is therefore handled on a case by case basis; however, so-called "back-room" or "soft" jobs are rare and are often contracted out if they do not belong to the company's core business activities. According to a report written by [START_REF] Volkoff | Efficaces à tout âge -Vieillissement démographique et activités de travail[END_REF], the ageing process is accompanied by an increase in certain pathologies such as articular problems and reduces the ability to withstand alternating day/night shift patterns. However, neither physical strength nor cognitive functions change significantly in people of working age [START_REF] Greller | In search of late career. A review of contemporary social science research applicable to the understanding of late career[END_REF]. On this last point, neurobiological research has shown that older employees mobilise different cerebral capacities than younger colleagues for certain tasks, particularly those involving memorisation, thus performing a "neuronal reorganisation" which allows them to compensate for performance deficits (Grady in [START_REF] Falkenstein | Le retour des seniors[END_REF]. They develop a "crystallised intelligence", i.e. a mode of reasoning that mobilises the memory and knowledge of the past in order to solve a problem [START_REF] Kanfer | Aging, adult development motivation[END_REF].

Furthermore, the problems in mainly tertiary economies are not expressed in the same ways as in predominantly industrial economies. This is not a question of minimising the sometimes pathological consequences of a lifetime spent working in difficult conditions; it is about stating that the argument cannot be valid for an entire population [START_REF] Greller | Careers at midlife and beyond: a fallow field in need of sustenance[END_REF].

Finally and above all, the ageing of people at work is a combination of natural biological ageing processes and the ageing that results from a number of environmental factors including the working environment in particular [START_REF] Teiger | Le vieillissement différentiel dans et par le travail. Un vieux problème dans un contexte recent[END_REF]. The implementation of career management practices and measures to improve working conditions and develop versatility has a major influence on the consequences of ageing. The over-fifties compensate for any decline in performance by employing adaptation strategies such as the economy of actions and movement, provided that they are allowed to do so by their work organisation (Volkoff et al, ibid.).

Our research into the strategies used for managing the over-fifties shows that few companies have implemented preventive reforms in order to facilitate work. More often than not, the management of incapacity is curative. On this subject, the HR manager of Flaco 8 , a manufacturing site for glass bottles, states that "Today we're paying for years of putting up with difficult working conditions: employees sought to negotiate bonuses in compensation for the difficulty of the work and personnel departments had very little interest in the subject".

The economic argument

The economic argument is also worthy of examination. In accordance with collective agreements, seniority is one of the criteria for wage increases. It might be imagined that the productivity of employees is adversely be affected with age; however, [START_REF] Gautié | Les travailleurs âgés face à l'emploi[END_REF] has shown that the productivity of employees who remain in employment does not, on average, seem to decline with age, thus confirming the findings of other studies [START_REF] Greller | In search of late career. A review of contemporary social science research applicable to the understanding of late career[END_REF]. In fact, the decline in performance depends on a complex combination of variables (individual and situational) that cannot be simply attributed to the single factor of age.

Another study concludes in a similar fashion by showing that employees over the age of 55 are most commonly found in older companies and are often assigned to posts lacking the latest equipment that would offer the best productivity (Aubert and Crépon, 2004). Ascribing declining productivity to the age factor alone would therefore seem to be an oversimplification of more complex phenomena.

The psychological argument

The psychological argument, especially the widely-held view concerning the "loss of motivation" in the over-fifties, also serves to justify the ousting of older employees. For the most part, our interviewees used two elements as the basis for identifying a loss of motivation.

The first relates to the demands of certain employees. Indeed, many of the HR managers interviewed reported that age policies have ended up creating an expectation amongst employees. The words of the HR manager at Avionics (maintenance of flying instruments for the aeronautical industry) are representative of a widely held point of view: "At 57 or 58 years old, I get people coming into my office and asking me: "Can you do anything for me?"

To put it plainly, they want us to find a way to get them early retirement. And they're right.

We've been doing it for so long, why not for them too?" Another HR manager plainly states that "we mustn't close our eyes to redundancy negotiated two or three years before retirement age: it exists and it's to everyone's advantage". Requests from employees to quit the world of work prematurely certainly exist. Should we take them to be a sign of a loss of motivation or more simply, the opportunity to benefit from existing provisions favoured by management and unions for many years? From our standpoint, much more than being the manifestation of supposed psychological predispositions, these requests reveal a social representation of the place of the over-fifties which, historically in France, has been created by a succession of provisions designed to help them leave the workplace. To us, their desire to stop working does not seem to represent an age-related psychological detachment from their employer. We see it as the negative effect of an apparently well-meaning measure, which reinforces the idea in everyone's minds that what the over-fifties rightly aspire to do is leave their employment.

A loss of motivation is also identified by visible manifestations of ways of working: a lack of enthusiasm, the expression of a certain nostalgia or the challenging of certain decisions. This raises the question of what we mean by a "loss of motivation". If we look at the levels of interest that might be aroused by different sources of motivation, it is certainly true that the same sources do not trigger the same reactions in everyone. Does a lack of desire for promotion in one's career really mean a lack of motivation? Is a lack of enthusiasm for innovation (or something that claims to be innovative) truly synonymous with giving up?

During the course of a study carried out on behalf of a company which focused precisely on the loss of motivation in the over-fifties, it was apparent that they did not feel "demotivated", but that they had different desires to younger employees. In our experience, the influence of anthropological screening extends as far as the definition of a correct way to express a motivation.

For [START_REF] Kanfer | Aging, adult development motivation[END_REF], the view that motivation declines with age is not supported by scientific evidence. Although a certain weariness might exist, we cannot ascribe an individual process to a phenomenon whose causes are partly organisational. Marbot clearly shows that the feeling of arriving at the end of one's professional life is as much an organisational process as a psychological one, as it is founded on a lack of support for the motivation process (2004). Conversely, a positive view of ageing employees can lead to the use of their experience in the framework of a tutorial system, a training role or consultancy tasks. A simpler way of showing them that their qualities are appreciated would be to maintain their allocation of resources.

TRUNCATED KNOWLEDGE OF PROFESSIONAL EXPERIENCE

It is normal to consider experience to be the major asset of senior staff. In the words of one of the HR managers we met, this acceptance could even be viewed as "trivial", due to the extent that "the received wisdom about experience is recurrently a message of success" (Astier, 2004, p. 34).

However, what do we do with this treasure that experience is supposed to represent? How is it promoted and accounted for in skills transmission processes? Our survey of management strategies for the over-fifties points to the rarity of measures taken to ensure the transfer of skills acquired by experienced staff, since only two companies out of the fifteen studied use such practices and none of the companies visited promotes the Validation of Acquired Experience9 . Let us also remember that in our study, the most commonly observed management strategies for the over-fifties aim to encourage these employees to leave their companies: a phenomenon that can also be observed at the national level through early retirement policies. These policies have not been slowed down by the fear of losing skills acquired through experience.

Experience, an undervalued asset

The most detailed study of the thinking concerning experience in the over-fifties indicates that a combination of several factors is undermining its value. Firstly, technological developments may render some of the oldest employees' skills obsolete. In some of the situations that we were told about, the youngest employees are being used as tutors for the oldest because they have a better knowledge of recent technologies than their older counterparts. Take Electronis, for example. This company manufactures electromechanical and electronic appliances (keyboards and special indicator lights designed for industry, beacon columns and safety light curtains). The manufacture of electromechanical appliances is declining in favour of electronic equipment, which offers greater added value. In the latter category, young graduates possess the qualifications needed for the manufacture of these products. Older employees with extensive experience in manufacturing electromechanical products are seeing a sharp downturn in their business. Eventually, there is a danger of it disappearing from the site altogether, with manufacturing being transferred to countries with lower salary costs. The HR manager is not replacing employees who retire and has launched a training plan designed to allow older employees to switch over to the more profitable electronics side. The youngest employees are therefore responsible for transferring skills to the oldest.

In other situations, automation is simplifying work and causing older employees to lose the benefits they might otherwise have gained from their experience. Char provides a good illustration of this phenomenon. This factory manufactures forklift trucks and has increased both its production and its workforce. While 350 employees were manufacturing 3,500 forklifts per year in 1984, the annual figure had risen to 25,000 forklifts for 600 employees in 2004. In twenty years, the company has progressed from a ratio of 10 trucks per person to 41 trucks per person. This rise in productivity can be mainly put down to designing products that are simpler to manufacture, requiring fewer operations and devising an assembly operation that is easier to carry out. Each new model of forklift truck is an opportunity to increase productivity.

By seeking to formalise, standardise and transmit expertise or develop versatility, contemporary management practices strip the most experienced employees of their knowledge in order to transmit it to others, even when these older employees have a wealth of valuable experience to offer. Thus, the HR manager at Davia has sought to identify the company's key employees: those with a monopoly of skills. The departure of these employees, who are deemed to be strategically important, is prepared approximately six months before they leave. However, it has become apparent that, on account of the company's skills development policy, few people are in sole possession of certain skills. Within the company, the adoption of the ISO 9001 standard (2002 version) has required the formalisation of skills as well as the creation of written procedures and instructions sheets. Standardisation and practices relating to Knowledge Management 10 are two contemporary managerial trends sharing the common aims of formalising and transmitting working procedures.

Paradoxically, policies designed to broaden or enrich work and organise career development internally could also end up undermining the skills of certain employees, as these skills become shared by everyone. In this respect, the management modes that we have identified, i.e. the tutorial system, conservation of memory and multiple skills development, which might be thought of as being beneficial, also have the unfortunate side-effect of depriving the overfifties of their status as holders of skills that are deemed to be strategically important.

Finally, the study of other situations shows that seniority does not guarantee the acquisition of expertise. To demonstrate this, the HR manager of Cendrillon cites the example of a young welder, recently recruited by the company, who was quick to understand the procedures and acquire expertise. Moreover, he discovered tricks of the trade that the older employees had not picked up on, because they had been happy to repeat the same tasks without questioning the efficiency of their actions. This example is a good illustration that experience is not a "byproduct of the activity" (Vincens, 2001, p. 22). The acquisition of experience requires workers to distance themselves from what they are doing, which has nothing to do with age. Thus, having "twenty years' experience" reveals little about the experience acquired by doing the work. This repeated observation questions the notion of professional experience.

10 Knowledge Management: a concept encompassing various practices concerning the formalisation of procedures and working methods in order to facilitate their transmission.

The invisible dimension of hidden experience

From what HR managers say, experience is represented as being the sum of the expertise relating to the work prescribed by the organisation, i.e. of learning experiences that are, on the whole, codifiable. However, we believe this notion to be incomplete because it does not include certain elements of acquired experience that are harder to quantify because they are invisible. Numerous authors refer to this experience, albeit in different terms and from slightly different perspectives. Grasser and Rosé consider that experience consists partly of expertise and partly of specific and tacit knowledge of the production process (2000). [START_REF] Aubret | Valorisation et validation de l'expérience professionnelle[END_REF] distinguish between the practices, i.e. "what is observable and verifiable", and their effects, which might "just as easily relate to spontaneous consciousness or perception as to the results of an analysis or an interpretation". Rouby and Thomas make a similar distinction, between "codifiable knowledge" and "tacit knowledge", which is harder to codify (2004). Mallet and Vernières also mention both of these dimensions. They believe that experience consists of a technical dimension and a social dimension (1981). The first dimension covers mastering the expertise acquired by performing a job, while the second focuses on integration into the environment, the appropriation of specific language, the development of professional "reflexes" and the learning of informal codes of behaviour.

Whether it is tacit, social or analytical in nature, little attention is paid to the invisible dimension of experience, even though this may be a major asset for the over-fifties. For example, this experience might be expressed by the appropriation of operating rules, the internalisation of certain rules of behaviour such as safety rules, the ability to form part of an informal internal network, the remembrance of previous procedures or tests and the recollection of how specific cases were handled in the past. Professional experience is not only an individual attribute. It is also, as several authors agree, the result of a person's analysis of the way in which he or she carries out his or her work (Astier, 2004;Schwartz, 2004;Vincens, 2001;Grasser and Rosé, 2000). This introspection can be encouraged by organisational measures. For example, certain participative measures such as quality circles might encourage teams to embark on a strategy of reflection which might otherwise not come naturally. Finally, the nature of jobs themselves provides opportunities to acquire varying degrees of experience. Not only do repetitive jobs offer few opportunities to learn when working, they also give few opportunities to consider or challenge working practices, whereas functional mobility and professional development do.

The value attributed to professional experience also relates back to the anthropological screening mentioned earlier in the paper. By studying the Validation of Acquired Experience (VAE), carried out by judging panels responsible for awarding DUTs (undergraduate diplomas in technology) and vocational bachelor's degrees, we have identified that VAE judging panels establish reference points in response to the unusual exercise of transforming experience into a form of entitlement to all or part of a diploma. They look for proof of technical skills (tasks, activities, posts, tools, etc.) and for indications of cognitive and social skills that are deemed to be essential in terms of eligibility for university diplomas. On this less tangible point, the judging panels perform a meta-analysis of the presentation, i.e. they focus their questions on the technical skills learnt on the job and assign values to the cognitive skills that have been acquired. By doing this, they are looking for indications of the candidates' cognitive abilities in how they talk and in the words they use in order to compare them to an informal reference standard. In the absence of an assessment tool, these comparisons act as decision-making criteria, both for the knowledge and know-how that are deemed to be useful, and for the candidate's modes of reasoning. This detour into VAE is useful as it shows that estimating the value of someone's professional experience is also the product of gauging their acquired cognitive and social skills.

Experience is never assessed in itself but in relation to a reference model. When this model has traits inspired by youth ("dynamism", "capacity for change", "familiarity with information technology", etc. are associated with young people -Guillemard, ibid.), this, rightly or wrongly, serves to diminish the value of the experience possessed by the overfifties.

FOCUSING THE PROBLEMS OF THE OVER-FIFTIES AT WORK ON

INDIVIDUALS

The received wisdom about the over-fifties at work, which underlies their exclusion from the workplace, is focused on individuals. The rarity of practices designed to enhance their value or transfer their experience, the absence of career development-except for managerial staff, the inadequacies of targeted training schemes and the limited number of measures aimed at improving working conditions are representative of organisations which, up to now, have shown little interest in the over-fifties population and which have thus helped to create the phenomenon they lament today.

Whether it is a question of physical problems caused by working in difficult conditions or of diminished motivation, the explanations for these phenomena can be reversed: not only could changing the focus of the explanation from the individual to the organisation help to improve our understanding of the deep-rooted causes of the problems, be they physical or supposedly psychological, it could also give the organisation more room for manoeuvre. This opens up the possibility of implementing measures designed to help keep the over-fifties in employment, while giving them favourable conditions in which to express their abilities.

The strategy of the Condor company shows the benefits of taking action before problems start to appear, as their research into the management of ageing dates back to 1990. A working group was established at the start of this process involving members of staff representative organisations including the Health, Safety and Working Conditions Committee, the planning department and supervisors. Condor manufactures rubber boots and its thinking focused on certain work stations at which conditions were very difficult (standing stations with repetitive movements). This brainstorming led to the introduction of flexibility (employees now change stations every two hours), reducing postural fatigue and improving remuneration via the interaction of different job categories. Another consequence was to act upon the rhythms of work, as the employees themselves now set the tempo for the conveyors carrying the boots, thus allowing them to regulate their rates, whereas this was previously set by the conveyor.

Finally, a U-shaped chain has replaced the linear chain in order to allow the employees to communicate and offer advice to their less experienced colleagues. No reductions in productivity have been observed, but the quality of products has improved and there is a greater feeling of working in better conditions.

CONCLUSION

The key message about the over-fifties shows that they do not correspond to the standard employee model. Victims of anthropological screening, they are ousted from their companies when the aim is to cut jobs and filtered out when they try to find new ones.

The management of the over-fifties by cutting their jobs is based on a vision centred on the individual employee. For each example, we have shown that the criticisms levelled at the over-fifties are also a result of their interactions with the employer. The same applies to health problems in the workplace. These are undeniably linked to natural ageing processes but their effects can be accelerated or reduced according to the company's practices. And the same can be said for "loss of motivation", which is not a foregone conclusion provided that appropriate measures are put into practice.

By from an interpretation of the behaviour of the "over-fifties" that is centred on the individual employee to one which is focused on the employee-employer relationship, we can open up the possibility of transforming our knowledge into action. Much more than the disappearance of age policies or the introduction of the "over-fifties" contract, neither of which have caused a revolution from an organisational point of view, the development of practices that are lacking today could prompt employers to consider the over-fifties in all of their variety and to evaluate them in terms of what they do and not on the basis of what we think they should be. For only by gaining recognition for the benefits of their experience and expertise, perfected over time, will the over fifties cease to represent the human face of restructuring operations.

  2004. Parler d'expérience, Formation et emploi, 88, October-December: 33-42 Aubert P. & Crépon B. 2004. La productivité des salariés âgés. Une tentative d'estimation, Economie et Statistiques, 368: 95-119

Ford was looking for workers of the "highest quality", capable of maintaining an intensive rhythm of work.

According to the Le Monde/Eurostat social indicator, the employment rate for the 55-64 year-olds in France stands at

38% at the end of 2006.

In the recent announcement concerning the Airbus job reduction plan, age-related measures were the first to be announced.

Association Régionale pour l'Amélioration des Conditions de Travail (Regional Association for the Improvement of Working Conditions). The ARACT Poitou-Charentes agency belongs to the network of the Association Nationale pour Amélioration des Conditions de Travail (National Association for the Improvement of Working Conditions).

Gestion Prévisionnelle des Emplois et Compétences (Predictive Management of Jobs and Skills)

Ford was looking for workers of the "highest quality", capable of maintaining an intensive rhythm of work.

The headline of the monthly publication "Liaisons Sociales" in October 2005 concerned finishing one's career at 45 years old!

In the interests of confidentiality, the names of the companies mentioned have been changed.

Since the French law enacted in 2002, professional experience can be validated as a requirement for all or part of a diploma.


Sarah A Dahab
email: sarah.dahab@telecom-sudparis.eu

Stephane Maag
email: stephane.maag@telecom-sudparis.eu

Suggesting Software Measurement Plans with unsupervised learning data analysis

Keywords: Software metrics, Software Measurement, Measurement Plan, SVM, X-MEANS;

Software measurement processes require to consider more and more data, measures and metrics. Measurement plans become complex, time and resource consuming, considering diverse kinds of software project phases. Experts in charge of defining the measurement plans have to deal with management and performance constraints to select the relevant metrics. They need to take into account a huge number of data though distributed processes. Formal models and standards have been standardized to facilitate some of these aspects. However, the maintainability of the measurements activities is still constituted of complex activities. In this paper, we aim at improving our previous work, which aims at reducing the number of needed software metrics when executing measurement process and reducing the expertise charge. Based on unsupervised learning algorithm, our objective is to suggest software measurement plans at runtime and to apply them iteratively. For that purpose, we propose to generate automatically analysis models using unsupervised learning approach in order to efficiently manage the efforts, time and resources of the experts. An implementation has been done and integrated on an industrial platform. Experiments are processed to show the scalability and effectiveness of our approach. Discussions about the results have been provided. Furthermore, we demonstrate that the measurement process performance could be optimized while being effective, more accurate and faster with reduced expert intervention.

Introduction

Software measurement is currently gaining in popularity and effectiveness due to several breakthrough advancements in data collecting techniques, data analysis, artificial intelligence, human factors, etc. [START_REF] Oivo | Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2018[END_REF]. Measurements are used in many research and industrial areas to monitor the involved processes in order to improve the production(s), reduce the costs (time and resources), manage the human efforts, etc. [START_REF] Dumke | Software measurement: current trends in research and practice[END_REF]. To tackle these purposes, diverse techniques have been developed and integrated in most of the industrial processes. From measurement plans defined by experts (often the project, software or system managers advised by engineers), these approaches collect an important amount of data that are semi-automatically analyzed. This analysis allows to raise issues, alarms to fix or improve the measured elements.

In a previous work [START_REF] Dahab | A novel formal approach to automatically suggest metrics in software measurement plans[END_REF], we conducted an interesting and promising research work to fully automate the definition of software measurement plans at runtime. Indeed, in most real case studies, that process is fixed in a sense that the expert measures all what he can and not necessarily what he needs. Then a huge amount of data are unnecessarily collected and analyzed. Our previous work has shown that measurement plans can be suggested and adjusted at runtime through an automatic learningbased methodology in reducing the amount of collected data. In this current novel work, we increase our efforts in the analysis and efficiency of software measurement by introducing unsupervised learning algorithm, and we specifically demonstrate the effectiveness of considering the clustering approach X-MEANS [START_REF] Pelleg | X-means: Extending kmeans with efficient estimation of the number of clusters[END_REF] to reduce further the management cost and improve the performance of such a process. We herein use X-MEANS to automatically generate correlations of a sample of data through clustering by generating a training file as input to our previous suggestion approach.

Software measurement is an empirical science which depends on the experience [START_REF] Fenton | Software metrics: roadmap[END_REF]. It is impossible to define a generic measurement analysis model. It depends on the software project, the used language, the used computer. Thereby, to evaluate a software, it is needed to know the context of the measured object, as well as, to analyze a software evaluation is needed to know the context. That is what makes difficult to automate a software measurement analysis. And this is to handle this lack that we propose to use an unsupervised learning technique, which will learn from a measurement dataset of a software to generate the corresponding analysis model and reduce the expert load and the related time cost.

In our novel work, our objective is to use this learning technique not only to reduce the time cost and the expert load by automatically generating a training file, but also to facilitate analysis model validation specific to software quality analysis and perhaps enable a more efficient future measurement process than in other older engineering systems, i.e, electricity, biology, physics, etc. Indeed, those have welldefined measurement plans like their analysis models allowing standard measurement protocol, effective and available to everyone.

Regarding our main contributions, they are summarized in the following:

• We use unsupervised learning approach as generator of analysis models in order to reduce the time cost, expert cost while improving the performance.

• We formally define our software measurement analysis and suggestion tool.

• And we experiment and assess our approach to suggest software metrics.

Preliminaries

This section defines the application domain, that is the software measurement. First, we briefly formally define the concept of software measurement, then we introduce the ISO/IEC 25000 standard on which we base our domain knowledge, at last we define the analysis context, the data model.

Software Measurement Definitions

Software measurement concepts have been formally defined in many research papers and standards, implemented in tools and are now used for several in-dustrial works. We provide few basic definitions that we use in our work.

A Measure is the calculation evaluating a software property (e.g., LoC). Formally, this is a function f : A → B| A ∈ X, B ∈ B that, from a set of measurable properties A of an object X (also named measurand in software measurement), assigns a value B of a set B. A Measurement is then a quantification of a measured property [START_REF] Fenton | Software metrics: a rigorous and practical approach[END_REF]. Formally, it refers to the result y of the measure f such as

y = f (A)| A ∈ X.
In our work, these functions have been formally used, implemented and integrated within an industrial platform. For that, other concepts need to be defined. A Metric is the formal specification of a measurement. It specifies the measurand, the measure(s) and the software property to be measured. Finally, since one of the main contributions of our work is to improve the software measurement plans at runtime, we define a Measure Plan as an ordered set of metrics. It is expected to be executed at a specific time t or during a well-defined duration (depending on the measurand(s), the platform, the users, the probes, etc.) and according to an ordered metrics sequence. Besides, they can be run sequentially or in parallel.

ISO/IEC 25000 -A Standardization of Software Measurement Process

We try to improve the software measurement process by using learning algorithms to reduce the costs of management and analysis. For that, we try to reduce, on one hand, the expertise charge using unsupervised learning algorithm and on the other hand, to optimize the measurement process performance by reducing its processing load.

In order to reduce the processing load, we proposed [START_REF] Dahab | A novel formal approach to automatically suggest metrics in software measurement plans[END_REF] a suggestion algorithm. The aim of this latter is to analyze a set of measurements during a period of time and according to the analysis result a suggestion of a new measurement plan is generated. This allows to reduce the processing load by executing at each time the metrics of interest according to the software needed instead of executing all the metrics each time.

To do this, it is necessary to determine the software properties to be analyzed and the corresponding metrics. Therefore, we base our work on the standard ISO/IEC 25000 [START_REF] Iso | Iec 25000 software and system engineeringsoftware product quality requirements and evaluation (square)-guide to square[END_REF] which defines within 4 divisions the software quality and the measurement of the quality of a software. Especially, the ISO/IEC 25010 (ISO/IEC, 2010) division defines the software properties, 8 for quality product, which describe the software quality. And the ISO/IEC 25020 [START_REF] Iso | Iec 25020 software and system engineeringsoftware product quality requirements and evaluation (square)-measurement reference model and guide[END_REF] division defines the measures (or metrics), more than 200, which give information on these properties.

Our correlation model explained in the next section is based on this model.

Software Measurements Model

The analyzed data are software measurements, more precisely the measurements values. These values give information on a software at time t. Definition 1 Measurement Plan: A measurement plan mp is the ordered set of metrics considered for the global measurement process and the correlation between the metrics and the evaluated software properties y. This is defined by the expert at the beginning of the process and it never changes. This is the context of our measurement process and the basis for the suggestion (see 3.1.3).

We define the ordered set m and the set of software properties y as:

m = {m 1 , ...m n }
(1) where n is the number of considered metrics and m i a unique metric.

y = {y 1 , ...y l }
(2) where l is the number of considered properties and y k a unique property.

The correlation C is defined as below:

C = (m i , y k ) (3)
where m i is the metric i and y k the property k. Some metrics are common to several properties while some metrics are specific to a single one. These correlations are used by the suggestion step (cf. 3.1.3). Finally a mp is defined as a set of metrics m, properties y and their correlations C:

       m = {m 1 , ...m n } y = {y 1 , ...y l } c = (m i , y k ) m i ∈ m and y k ∈ y (4)
Definition 2 Vector ì v: A vector is the analyzed data model. This is a set of different metrics values. Each field of the vector is a value x i of a specific metric executed at time t. Thus, a vector contains a set of information on a software at time t and it is defined as:

ì v = {x 1 , ...x n }
(5) Where n is the number of metrics in the measurement plan. These metrics give information on one or several software properties. This correlation between software properties and metrics is herein used for the suggestion of measurement plans.

Definition 3 Feature: A feature is a field of a vector. It refers to the metric i associated to the field i of a vector ì v. A feature is unique. The place of metrics in the vector is fixed. So a feature is a value x i in a vector ì v Definition 4 Class: A class is a cluster of vectors. It refers to a group of vectors with close values. In a broader sense, it refers to a group of vectors providing the same information type on the software and is defined as below:

class = ì v 1 , ...ì v p ( 6
)
Where p is the number of vectors classified in the class.

Our Unsupervised Learning approach

In this paper, we use our suggestion approach to improve the result of our previous works, well defined in the paper [START_REF] Dahab | A novel formal approach to automatically suggest metrics in software measurement plans[END_REF], where our purpose was to generate flexible measurement plans adapted to the software need, thus allowing to reduce the analysis cost and the one of the software measurement process.

However, this approach is still dependent to the expert for the initialization step, especially for the elaboration of the training file. As a reminder, this file is used to train the classifier and it defines the correlation between vectors and classes. So, the analysis model is manually done. Thus, the cost time of this step is high when the samples to classify are highly numerous.

In order to handle this lack, we propose to use an unsupervised learning algorithm to generate automatically an analysis model. From an unlabeled software measurements sample, a labeled one is generated. This output is then used as training file to train the classifier used for the analysis and suggestion steps.

The purpose is to use a clustering algorithm. It will group in clusters the similar vectors of measurements then according to the clustering result, the expert will associate to each cluster a set of metrics to suggest (see 2.2). Herein, the expert intervention only appears for determinating the correlation between classes corresponding to the clusters, and set of metrics.

Our improved suggestion approach is based on three procedures:

• The elaboration, through software measurements clustering based on unsupervised learning ap-proach X-MEANS, of an analysis model (or mapping system as called in our previous work), herein considered as the initial measurement plan mp.

• An analysis procedure, which aims to highlight a software property of interest through a software metrics classification, based on a learning technique, herein SVM.

• A suggestion of metrics based on the mp, the analysis result and the features selection procedure, which aim to determine the needed metrics (nm), for the conservation of information and based on the learning technique RFE.

In the next sections, we briefly describe our metrics suggestion approach that is improved and formalized. Then we introduce the X-MEANS technique.

Our Metrics Suggester Approach

This approach is based on three procedures :

• The initialization of the measurement plan,

• The analysis of measurements data through the supervised classification algorithm SVM,

• And the suggestion of novel measurement plans.

The initial Measurement Plan

The initial measurement plan is the basis of our suggestion algorithm. Indeed, the analysis and the suggestion are based on it.

The initial measurement plan is elaborated by the expert and it defines the observed set of metrics, the corresponding software properties and the mandatory metrics, the ones that must always be in the suggested measurement plans.

This MP is the definition of the measurement context : what is observed by the software properties, how it is observed by the set of metrics related to the properties and the mandatory ones.

The set of metrics groups all the metrics that could be computed during all the measurement process. Thus, the suggestion is a subset of this set of metrics.

The analysis

The analysis consists in classifying a set of measurement data, more precisely a set of vectors ì v. Each vector is classified in one class which refers to a software property defined in the initial measurement plan and related to ISO/IEC 25000. To classify the data we use a supervised learning algorithm SVM.

SVM is a linear classifier trained through a training file. This file is elaborated by the expert and it corresponds to a manual classification. Indeed, the expert classifies a set of vectors by labelling each vector by a class. Then, a classifier is trained according to this manual classification. Thereby a specific classifier is then used for a specific analysis. The training file corresponds to the initial measurement plan. The used labels should correspond to the defined class as the set of metrics classified. In fact, the suggestion is based on this specific classification.

It means that each time we want to change the context of the measurement process, a new training file should be done by an expert to generate the corresponding classifier. And this was the main limitation of our approach. Despite an automated and "smart" analysis, our approach is still highly dependent to the expert and quite costly in time.

Suggestion

The suggestion is based on the classification result and the initial measurement plan.

The result of the classification is a set of clusters. The number of clusters is the number of defined class and the data in the clusters is the set of vectors gathering during the measurement process. From this set of clusters, we choose the one with the largest number of vectors classified as the class of interest. Then, we add in the new measurement plan the set of metrics corresponding to the class of interest defined in the initial measurement plan. If all the vectors are classified in the same class, we thus suggest all the metrics.

Next, in the first case, we determine the metrics which were necessary for the classification by using the RFE algorithm [START_REF] Gao | Choosing software metrics for defect prediction: an investigation on feature selection techniques[END_REF]. The RFE algorithm used the classifier and the used data to select the features which allowed the classification result. Once the selection is done, we add the selected features in the new measurement plan.

Finally, we add the mandatory metrics defined in the initial measurement if these latter were not selected by the previous steps. Then according to these steps we generate the new measurement plan as a suggestion.

This procedure is formally described below, by the Algorithm 1. It takes as input the initial measurement plan, herein called mp, the trained classifier f and the set of vectors to be analyzed {ì v}.

Where mp is the suggested measurement plan, mm the defined mandatory metrics and f s the feature selection algorithm (RFE).

In order to reduce the load of the expert, we aim at improving this approach by using an unsupervised

Algorithm 1 Metrics Suggestion

Input mp, f , {ì v} 1: Output mp 2: y ← array() 3: n f ← array() 4: for each ì v in {ì v} do 5:

y ← f (ì v) 6: end for 7: if y == 0 or y without duplicate == 1 then 8:

return mp 9: else 10:

n f ← f s( f , {ì v}) 11: mp ← m f +mp[most common(y)]+mp[mm] 12:
return mp 13: end if learning technique to generate automatically the training file. The advantage of using this latter is to reduce the expert cost, but also the dependency of an expert. Indeed, as the software measurement is an empirical science, it depends on the experience, on the software or on the property evaluated. There is as much model as there is software project. Thereby, our purpose is to use learning clustering algorithm X-MEANS as expert to generate automatically the training file according to a measurement dataset of the evaluated project. The expert would only intervene to define the initial measurement plan according to the result of X-MEANS application.

Unsupervised-based analysis model elaboration

X-MEANS [START_REF] Pelleg | X-means: Extending kmeans with efficient estimation of the number of clusters[END_REF] is a clustering algorithm, more precisely it is an extension of the K-MEANS algorithm. X-MEANS splits into k clusters a sample of data without initialization of the number of cluster k. It determines the best k clusters by minimizing the intercluster similarity and satisfying the Bayesian Information Criterion BIC score.

For that, it dertermines 2 initial clusters by defining randomly 2 centrods, then assigns to each data the closest centrod. Then, it updates the centrods according to the sum of distances of each cluster. This distance D should be the smallest. Finally, it splits each cluster in two clusters and go to the previous step to have the lowest inter-cluster. A low inter-cluster similarity is ensured by assigning a data to the cluster whose distance to its center is the smallest. Thus, it tends to minimize this following function D.

D = k i=1 n j=1 |c i , x i j | (7)
Before each split the BIC score of each cluster model is computed. As example, the initial number of cluster is 2. So we have 2 clusters, c and i, with one centrod each, k = 1. For each cluster we compute its BIC score. Then we split each cluster in two sub clusters, that's mean k = 2 for each one. And then we compute the new BIC score of c and i with k = 2. If this score is lower than the previous one, we keep the cluster with k = 1. Else we split another time each sub cluster in two sub-sub clusters etc. So, if BIC(c, k = 1) > BIC(c, k = 2) we keep c with k = 1. And if BIC(i, k = 1) < BIC(i, k = 2) we keep i with k = 2 and we split each sub cluster of i in two sub sub clusters while c remains unchanged. After the splitting process this score is used to determine the best model : the one with the higher BIC score.

Once the clustering is done, we have a labeled dataset. From this labeled dataset the expert design the correlations between the clusters and set of metrics, then it is used as training dataset to train the classifier.

Experiment

Our analysis and suggestion tool is built as a web application as illustrated in the Figure 1. The architecture is organized around the machine learning unit (ML tool), which regroups the classification and feature selection algorithms. The first one is used to train the classifier, through the training file, and then to analyze the data by classifying it according to the trained classifier; the second one is used to determine the necessary features (herein metrics) to the classification. We use this latter to determine dynamically the mandatory metrics for the next analysis. The library used to develop the learning algorithms is scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF].

As our work is taking part of a European project MEASURE, its implementation has been integrated in the related industrial platform as an analysis tool.

Industrial MEASURE Platform Integration

The MEASURE Platform1 is the research result of the European project ITEA3 MEASURE2 . This project aims to improve the whole software measurement processes. For that, this platform proposes a database as storage of software metrics specified and developed according to the standard language SMM; a storage of measurement result; a coverage of all the software engineering processes; and analysis tools. As described in the Figure 2, there is the main measure platform which enables the communication between the different stores and the analysis tool, the measurement tools and the agent platform. The measurement tools allow to use other external measurement tools. And the agent platform allows a local execution of the metrics with a storage of results in the platform.

Our Metrics Suggester tool is integrated in the platform, as shown in the Figure 3, by using the REST API of the platform. This latter allows to connect our analysis tool to the platform, to gather stored measurements and to generate a dashboard from the platform.

In the next section, we will present the results of our improved approach for automatically generating a software measurement analysis model based on experience and measurements data history.

Automated analysis model generation

For evaluating our approach, we used a real industrial use case provided by one of the MEASURE partners. This one is based on a modelling tool suite. The analysis of this tool focuses on the developed Java code.

Experimental Setup

The considered set of metrics for the measurement process includes 13 metrics giving information on 3 software properties, as described in Table 4. This M P is defined by the measurement context : the observed metrics during all the processes and the mandatory ones. The properties or classes give information on what is evaluated, but the actual number of classes will be determined by the X-MEANS algorithm. In fact, the initial measurement plan will be defined according to the result of the X-MEANS execution, the expert will define the correlations between subsets of metrics and clusters.

The metrics related to the Maintainability property give information on the quality of the code. The ones related to Reliability give information on the reliability of the services and the Security ones are about the vulnerabilities in the code.

In order to execute X-MEANS, we generate a file with a fixed amount of data and a fixed number of group corresponding to a vector type : the data are vectors with values which correspond to a property. For example, the fields corresponding to the metrics related to the maintainability property are high and the others are low, herein called vector-type. The objectives are twofold, first to verify if the clustering result matches with the expectation and if the suggestion still provides correct results with the automated labeled data set as input training file.

Clustering Results

As depicted in the Figure 5, the data are homogeneously distributed in the files 1 and 2: there is the same amount of vector types in each group. A group is a vector-type set. Finally, the data in the files 3 and 4 are heterogeneously distributed. There is a different amount of vectors in each group.

The column Data gives the amount of vectors per file and the column Distribution gives the number of vector-type.

Regarding the clustering result, we can see that when the file is too small, the clustering accuracy is not high. Indeed, the file 1 with 50 vectors and 3 vector-type is grouped in two homogeneous clusters while we expected 3 groups. But with files containing more data, the clustering result is better and promising. The accuracy result is better and they correspond to the expectations: the number of clusters corresponds to those in the groups and the distribution of the vectors in the clusters complies with those in the groups.

To conclude, X-MEANS shows a good performance to learn as an expert from experiences and to provide a reliable analysis model with considerable time savings. But also, this can be used as models validator, in order to verify the validity of data model.

Suggestion results

Finally, the initial measurement plan is defined by the expert according the previous step result. In fact, the expert will add to the MP presented in the Figure 1 the correlation between clusters and a metrics subset.

Once the initial mp is defined, we train the classifier with the file 3. Then, as suggestion experiment, we use as input files to analyze, a dataset of 50000 unclassified vectors divided in 10 subsets of 5000 unclassified vectors. The objective is to see if the suggestion provides correct plans (of metrics).

The Figure 6 shows the results of suggestions based on the previous analysis model. The results show a dynamic suggestion of mp. Each mp is between 5 and 13 metrics. There is no convergence (e.g., deadlock or undesired fixity in the generated plans) and the suggested mp evolves continuously according to the dataset values.

Related Works

Standardization institutes put lots of efforts in defining. They focus on the definition and formalization of software quality models such as the ISO9126 that qualifies and quantifies functional and non-functional properties with software metrics (Car- vallo and Franch, 2006). Besides, two other standardization institutes worked in that way to propose two commonly used norms namely ISO/IEC25010 [START_REF] Kitchenham | What's up with software metrics? -A preliminary mapping study[END_REF] and OMG SMM [START_REF] Bouwers | Evaluating usefulness of software metrics: an industrial experience report[END_REF] in order to guide the measurement plan specification. These two last standards have been reviewed by the research and industrial community, and are adapted, integrated and applied in many domains. In the research literature, several works on software metrics selection for software quality have been provided [START_REF] Gao | Choosing software metrics for defect prediction: an investigation on feature selection techniques[END_REF]. Recent techniques based on learning approaches have been proposed. Most of them are dedicated to software defect prediction [START_REF] Shepperd | Researcher bias: The use of machine learning in software defect prediction[END_REF][START_REF] Macdonald | Software defect prediction from code quality measurements via machine learning[END_REF], [START_REF] Laradji | Software defect prediction using ensemble learning on selected features[END_REF], metrics selection [START_REF] Bardsiri | Machine learning methods with feature selection approach to estimate software services development effort[END_REF] or even Software testing [START_REF] Kim | Machine learning frameworks for automated software testing tools: A study[END_REF]. However, even if these techniques have introduced considerable progress to improve the software quality, they have still some limitations. The measurement plan is still manually fixed by the project manager or the experts in charge of its definition. Furthermore, the implementation of the measures is dependent on the developer and reduce the scalability, maintainability and the interoperability of the measurement process.

While a current study shows the lacks in the use of learning technique for software measurement analysis [START_REF] Hentschel | Big data benefits for the software measurement community[END_REF], there are in literature some works which use supervised learning algorithms, especially for software defect prediction [START_REF] Laradji | Software defect prediction using ensemble learning on selected features[END_REF][START_REF] Shepperd | Researcher bias: The use of machine learning in software defect prediction[END_REF] or for prioritize software metrics [START_REF] Shin | Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities[END_REF]. Indeed, there are a lot of software metrics, and currently the measurement processes execute all the metrics continuously. This latter shows that we can prioritize the metrics and thus reduce the number of metrics to be executed.

There are also works which propose to use un-supervised learning technique to estimate the quality of software [START_REF] Zhong | Unsupervised learning for expert-based software quality estimation[END_REF] as "expert-based".

They also propose to base on clustering techniques to analyze software quality (Zhong et al., 2004a). Other works propose to combine supervised and unsupervised learning techniques to predict the maintainability of an Oriented Object software [START_REF] Liu | Applications of support vector mathine and unsupervised learning for predicting maintainability using object-oriented metrics[END_REF]. But all of these works focus on the analysis or prediction of one software property. The aim of our approach is to allow the less of expert dependency to evaluate all the software engineering process, and to suggest flexible mp continuously according to the software need.

Conclusion & Perspectives

In this paper, we proposed to improve our previous work by reducing the expert dependency to the management of the analysis process. For that, we propose to use an unsupervised learning algorithm X-MEANS to take the place of the expert and to generate automatically an analysis model by learning from an historical database. The objective is to reduce the management cost, and the time cost.

Well implemented and experimented, this approach shows the possibility to generate a reliable model with a low time cost, and also to verify the validity of manual models.

The promising results demonstrate us the beneficial contribution of using learning techniques in the software measurement area. Thereby, as perspective, it could be interesting to analyze the differences between automated models and manual models and also to increase the independence to the expert by generating automatically the correlations between clusters and metrics subsets. A statistic method on the weight of features could be envisaged in future works.

Figure 1 :

 1 Figure 1: Our Metrics Suggester tool architecture.

Figure 2 :

 2 Figure 2: Overview of the MEASURE platform.

Figure 3 :

 3 Figure 3: Industrial integration of our Metrics Suggester tool.

Figure 4 :

 4 Figure 4: Measurement Plan.

Figure 5 :

 5 Figure 5: Unsupervised clustering results.

Figure 6 :

 6 Figure 6: Suggestions results.

https://github.com/ITEA3-Measure

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