Improving automation in model-driven engineering using examples

Cette thèse a pour but d’améliorer l’automatisation dans l’ingénierie dirigée par les modèles (MDE pour Model Driven Engineering). MDE est un paradigme qui promet de réduire la complexité du logiciel par l’utilisation intensive de modèles et des transformations automatiques entre modèles (TM). D’une façon simplifiée, dans la vision du MDE, les spécialistes utilisent plusieurs modèles pour représenter un logiciel, et ils produisent le code source en transformant automatiquement ces modèles. Conséquemment, l’automatisation est un facteur clé et un principe fondateur de MDE. En plus des TM, d’autres activités ont besoin d’automatisation, e.g. la définition des langages de modélisation et la migration de logiciels. Dans ce contexte, la contribution principale de cette thèse est de proposer une approche générale pour améliorer l’automatisation du MDE. Notre approche est basée sur la recherche méta-heuristique guidée par les exemples. Nous appliquons cette approche sur deux problèmes importants de MDE, (1) la transformation des modèles et (2) la définition précise de langages de modélisation. Pour le premier problème, nous distinguons entre la transformation dans le contexte de la migration et les transformations générales entre modèles. Dans le cas de la migration, nous proposons une méthode de regroupement logiciel (Software Clustering) basée sur une méta-heuristique guidée par des exemples de regroupement. De la même façon, pour les transformations générales, nous apprenons des transformations entre modèles en utilisant un algorithme de programmation génétique qui s’inspire des exemples des transformations passées. Pour la définition précise de langages de modélisation, nous proposons une méthode basée sur une recherche méta-heuristique, qui dérive des règles de bonne formation pour les méta-modèles, avec l’objectif de bien discriminer entre modèles valides et invalides. Les études empiriques que nous avons menées, montrent que les approches proposées obtiennent des bons résultats tant quantitatifs que qualitatifs. Ceux-ci nous permettent de conclure que l’amélioration de l’automatisation du MDE en utilisant des méthodes de recherche méta-heuristique et des exemples peut contribuer à l’adoption plus large de MDE dans l’industrie à là venir.

Model-Based Reasoning: A Principled Approach for Software Engineering

Lee M.H., Model-Based Reasoning: A Principled Approach for Software Engineering, Software - Concepts and Tools,19(4), pp179-189, 2000.

Integrating Sensor-Network Research and Development into a Software Engineering Curriculum

The emergence of a sensor-networked world produces a clear and urgent need for well-planned, safe and secure software engineering. It is the role of universities to prepare graduates with the knowledge and experience to enter the work-force with a clear understanding of software design and its application to the future safety of computing. The snBench (Sensor Network WorkBench) project aims to provide support to the programming and deployment of Sensor Network Applications, enabling shared sensor embedded spaces to be easily tasked with various sensory applications by different users for simultaneous execution. In this report we discus our experience using the snBench research project as the foundation for semester-long project in a graduate level software engineering class at Boston University (CS511).

Enhancing Security Requirements Engineering by Organisational Learning

More and more software projects today are security-related in one way or the other. Requirements engineers often fail to recognise indicators for security problems which is a major source of security problems in practice. Identifying security-relevant requirements is labour-intensive and errorprone. In order to facilitate the security requirements elicitation process, we present an approach supporting organisational learning on security requirements by establishing company-wide experience resources, and a socio-technical network to benefit from them. The approach is based on modelling the flow of requirements and related experiences. Based on those models, we enable people to exchange experiences about security-requirements while they write and discuss project requirements. At the same time, the approach enables participating stakeholders to learn while they write requirements. This can increase security awareness and facilitate learning on both individual and organisational levels. As a basis for our approach, we introduce heuristic assistant tools which support reuse of existing security-related experiences. In particular, they include Bayesian classifiers which issue a warning automatically when new requirements seem to be security-relevant. Our results indicate that this is feasible, in particular if the classifier is trained with domain specific data and documents from previous projects. We show how the ability to identify security-relevant requirements can be improved using this approach. We illustrate our approach by providing a step-by-step example of how we improved the security requirements engineering process at the European Telecommunications Standards Institute (ETSI) and report on experiences made in this application.

A methodological research on software engineering applied to design of smart grids using a complex system approach

[ES] El reto de conseguir una red eléctrica más eficiente pasa por la introducción masiva de energías renovables en la red eléctrica, disminuyendo así las emisiones de CO2. Por ello, se propone no sólo controlar la producción, como se ha hecho hasta ahora, sino que también se propone controlar la demanda. Por ello, en esta investigación se evalúa el uso de la Ingeniería Dirigida por Modelos para gestionar la complejidad en el modelado de redes eléctricas, la Inteligencia de Negocio para analizar la gran cantidad de datos de simulaciones y la Inteligencia Colectiva para optimizar el reparto de energía entre los millones de dispositivos que se encuentran en el lado de la demanda.

An Active Learning Module for an Introduction to Software Engineering Course

Many schools do not begin to introduce college students to software engineering until they have had at least one semester of programming. Since software engineering is a large, complex, and abstract subject it is difficult to construct active learning exercises that build on the students’ elementary knowledge of programming and still teach basic software engineering principles. It is also the case that beginning students typically know how to construct small programs, but they have little experience with the techniques necessary to produce reliable and long-term maintainable modules. I have addressed these two concerns by defining a local standard (Montana Tech Method (MTM) Software Development Standard for Small Modules Template) that step-by-step directs students toward the construction of highly reliable small modules using well known, best-practices software engineering techniques. “Small module” is here defined as a coherent development task that can be unit tested, and can be car ried out by a single (or a pair of) software engineer(s) in at most a few weeks. The standard describes the process to be used and also provides a template for the top-level documentation. The instructional module’s sequence of mini-lectures and exercises associated with the use of this (and other) local standards are used throughout the course, which perforce covers more abstract software engineering material using traditional reading and writing assignments. The sequence of mini-lectures and hands-on assignments (many of which are done in small groups) constitutes an instructional module that can be used in any similar software engineering course.

On Software Engineering Repositories and Their Open Problems

In the last decade, a large number of software repositories have been created for different purposes. In this paper we present a survey of the publicly available repositories and classify the most common ones as well as discussing the problems faced by researchers when applying machine learning or statistical techniques to them.

The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments

All meta-analyses should include a heterogeneity analysis. Even so, it is not easy to decide whether a set of studies are homogeneous or heterogeneous because of the low statistical power of the statistics used (usually the Q test). Objective: Determine a set of rules enabling SE researchers to find out, based on the characteristics of the experiments to be aggregated, whether or not it is feasible to accurately detect heterogeneity. Method: Evaluate the statistical power of heterogeneity detection methods using a Monte Carlo simulation process. Results: The Q test is not powerful when the meta-analysis contains up to a total of about 200 experimental subjects and the effect size difference is less than 1. Conclusions: The Q test cannot be used as a decision-making criterion for meta-analysis in small sample settings like SE. Random effects models should be used instead of fixed effects models. Caution should be exercised when applying Q test-mediated decomposition into subgroups.

Quantitative Determination of the Relationship between Internal Validity and Bias in Software Engineering Experiments: Consequences for Systematic Literature Reviews

Quality assessment is one of the activities performed as part of systematic literature reviews. It is commonly accepted that a good quality experiment is bias free. Bias is considered to be related to internal validity (e.g., how adequately the experiment is planned, executed and analysed). Quality assessment is usually conducted using checklists and quality scales. It has not yet been proven;however, that quality is related to experimental bias. Aim: Identify whether there is a relationship between internal validity and bias in software engineering experiments. Method: We built a quality scale to determine the quality of the studies, which we applied to 28 experiments included in two systematic literature reviews. We proposed an objective indicator of experimental bias, which we applied to the same 28 experiments. Finally, we analysed the correlations between the quality scores and the proposed measure of bias. Results: We failed to find a relationship between the global quality score (resulting from the quality scale) and bias; however, we did identify interesting correlations between bias and some particular aspects of internal validity measured by the instrument. Conclusions: There is an empirically provable relationship between internal validity and bias. It is feasible to apply quality assessment in systematic literature reviews, subject to limits on the internal validity aspects for consideration.

Balancing software engineering education and industrial needs

In the world of information and communications technologies the demand for professionals with software engineering skills grows at an exponential rate. On this ground, we have conducted a study to help both academia and the software industry form a picture of the relationship between the competences of recent graduates of undergraduate and graduate software engineering programmes and the tasks that these professionals are to perform as part of their jobs in industry. Thanks to this study, academia will be able to observe which skills demanded by industry the software engineering curricula do or do not cater for, and industry will be able to ascertain which tasks a recent software engineering programme graduate is well qualified to perform. The study focuses on the software engineering knowledge guidelines provided in SE2004 and GSwE2009, and the job profiles identified by Career Space.

Configuration management and product lines to enhance the replication process in software engineering

This research is concerned with the experimental software engineering area, specifically experiment replication. Replication has traditionally been viewed as a complex task in software engineering. This is possibly due to the present immaturity of the experimental paradigm applied to software development. Researchers usually use replication packages to replicate an experiment. However, replication packages are not the solution to all the information management problems that crop up when successive replications of an experiment accumulate. This research borrows ideas from the software configuration management and software product line paradigms to support the replication process. We believe that configuration management can help to manage and administer information from one replication to another: hypotheses, designs, data analysis, etc. The software product line paradigm can help to organize and manage any changes introduced into the experiment by each replication. We expect the union of the two paradigms in replication to improve the planning, design and execution of further replications and their alignment with existing replications. Additionally, this research work will contribute a web support environment for archiving information related to different experiment replications. Additionally, it will provide flexible enough information management support for running replications with different numbers and types of changes. Finally, it will afford massive storage of data from different replications. Experimenters working collaboratively on the same experiment must all have access to the different experiments.

Using configuration management and product line software paradigms to support the experimentation process in software engineering.

There is no empirical evidence whatsoever to support most of the beliefs on which software construction is based. We do not yet know the adequacy, limits, qualities, costs and risks of the technologies used to develop software. Experimentation helps to check and convert beliefs and opinions into facts. This research is concerned with the replication area. Replication is a key component for gathering empirical evidence on software development that can be used in industry to build better software more efficiently. Replication has not been an easy thing to do in software engineering (SE) because the experimental paradigm applied to software development is still immature. Nowadays, a replication is executed mostly using a traditional replication package. But traditional replication packages do not appear, for some reason, to have been as effective as expected for transferring information among researchers in SE experimentation. The trouble spot appears to be the replication setup, caused by version management problems with materials, instruments, documents, etc. This has proved to be an obstacle to obtaining enough details about the experiment to be able to reproduce it as exactly as possible. We address the problem of information exchange among experimenters by developing a schema to characterize replications. We will adapt configuration management and product line ideas to support the experimentation process. This will enable researchers to make systematic decisions based on explicit knowledge rather than assumptions about replications. This research will output a replication support web environment. This environment will not only archive but also manage experimental materials flexibly enough to allow both similar and differentiated replications with massive experimental data storage. The platform should be accessible to several research groups working together on the same families of experiments.

Applying the experimental paradigm to software engineering

En este keynote, la Prof. Juristo describe el paradigma experimental y cómo podría aplicarse a la ingeniería del software, destacando los desafíos de su aplicación y los logros conseguidos hasta el momento.

The Innovation Sources in Software Engineering: A Systematic Literature Review

During the last century many researches on the business, marketing and technology fields have developed the innovation research line and large amount of knowledge can be found in the literature. Currently, the importance of systematic and openness approaches to manage the available innovation sources is well established in many knowledge fields. Also in the software engineering sector, where the organizations need to absorb and to exploit as much innovative ideas as possible to get success in the current competitive environment. This Master Thesis presents an study related with the innovation sources in the software engineering eld. The main research goals of this work are the identication and the relevance assessment of the available innovation sources and the understanding of the trends on the innovation sources usage. Firstly, a general review of the literature have been conducted in order to define the research area and to identify research gaps. Secondly, the Systematic Literature Review (SLR) has been proposed as the research method in this work to report reliable conclusions collecting systematically quality evidences about the innovation sources in software engineering field. This contribution provides resources, built-on empirical studies included in the SLR, to support a systematic identication and an adequate exploitation of the innovation sources most suitable in the software engineering field. Several artefacts such as lists, taxonomies and relevance assessments of the innovation sources most suitable for software engineering have been built, and their usage trends in the last decades and their particularities on some countries and knowledge fields, especially on the software engineering, have been researched. This work can facilitate to researchers, managers and practitioners of innovative software organizations the systematization of critical activities on innovation processes like the identication and exploitation of the most suitable opportunities. Innovation researchers can use the results of this work to conduct research studies involving the innovation sources research area. Whereas, organization managers and software practitioners can use the provided outcomes in a systematic way to improve their innovation capability, increasing consequently the value creation in the processes that they run to provide products and services useful to their environment. In summary, this Master Thesis research the innovation sources in the software engineering field, providing useful resources to support an effective innovation sources management. Moreover, several aspects should be deeply study to increase the accuracy of the presented results and to obtain more resources built-on empirical knowledge. It can be supported by the INno- vation SOurces MAnagement (InSoMa) framework, which is introduced in this work in order to encourage openness and systematic approaches to identify and to exploit the innovation sources in the software engineering field.

Some framework ideas for software engineering education

It is difficult, if not impossible, to find something that is not changing in computer technology: circuits, architectures, languages, methods, fields of application ... The "central object" itself of this brand of engineering, software, represents such a diverse reality (many objects) that the fact that it has only one name gives rise to considerable confusion. This issue, among others, was taken up by Fox (1) and, at this point, I would like to underline that it is more of a pragmatic issue than an academic one. Thus, Software Engineering Education moves in an unstable, undefined'world. This axiom governs and limits the. validity of all educational proposals in the area of Software Engineering and, thereforer all the ideas presented in this paper.