Towards using intelligent techniques to assist software specialists in their tasks

L’automatisation et l’intelligence constituent des préoccupations majeures dans le domaine de l’Informatique. Avec l’évolution accrue de l’Intelligence Artificielle, les chercheurs et l’industrie se sont orientés vers l’utilisation des modèles d’apprentissage automatique et d’apprentissage profond pour optimiser les tâches, automatiser les pipelines et construire des systèmes intelligents. Les grandes capacités de l’Intelligence Artificielle ont rendu possible d’imiter et même surpasser l’intelligence humaine dans certains cas aussi bien que d’automatiser les tâches manuelles tout en augmentant la précision, la qualité et l’efficacité. En fait, l’accomplissement de tâches informatiques nécessite des connaissances, une expertise et des compétences bien spécifiques au domaine. Grâce aux puissantes capacités de l’intelligence artificielle, nous pouvons déduire ces connaissances en utilisant des techniques d’apprentissage automatique et profond appliquées à des données historiques représentant des expériences antérieures. Ceci permettra, éventuellement, d’alléger le fardeau des spécialistes logiciel et de débrider toute la puissance de l’intelligence humaine. Par conséquent, libérer les spécialistes de la corvée et des tâches ordinaires leurs permettra, certainement, de consacrer plus du temps à des activités plus précieuses. En particulier, l’Ingénierie dirigée par les modèles est un sous-domaine de l’informatique qui vise à élever le niveau d’abstraction des langages, d’automatiser la production des applications et de se concentrer davantage sur les spécificités du domaine. Ceci permet de déplacer l’effort mis sur l’implémentation vers un niveau plus élevé axé sur la conception, la prise de décision. Ainsi, ceci permet d’augmenter la qualité, l’efficacité et productivité de la création des applications. La conception des métamodèles est une tâche primordiale dans l’ingénierie dirigée par les modèles. Par conséquent, il est important de maintenir une bonne qualité des métamodèles étant donné qu’ils constituent un artéfact primaire et fondamental. Les mauvais choix de conception, ainsi que les changements conceptuels répétitifs dus à l’évolution permanente des exigences, pourraient dégrader la qualité du métamodèle. En effet, l’accumulation de mauvais choix de conception et la dégradation de la qualité pourraient entraîner des résultats négatifs sur le long terme. Ainsi, la restructuration des métamodèles est une tâche importante qui vise à améliorer et à maintenir une bonne qualité des métamodèles en termes de maintenabilité, réutilisabilité et extensibilité, etc. De plus, la tâche de restructuration des métamodèles est délicate et compliquée, notamment, lorsqu’il s’agit de grands modèles. De là, automatiser ou encore assister les architectes dans cette tâche est très bénéfique et avantageux. Par conséquent, les architectes de métamodèles pourraient se concentrer sur des tâches plus précieuses qui nécessitent de la créativité, de l’intuition et de l’intelligence humaine. Dans ce mémoire, nous proposons une cartographie des tâches qui pourraient être automatisées ou bien améliorées moyennant des techniques d’intelligence artificielle. Ensuite, nous sélectionnons la tâche de métamodélisation et nous essayons d’automatiser le processus de refactoring des métamodèles. A cet égard, nous proposons deux approches différentes: une première approche qui consiste à utiliser un algorithme génétique pour optimiser des critères de qualité et recommander des solutions de refactoring, et une seconde approche qui consiste à définir une spécification d’un métamodèle en entrée, encoder les attributs de qualité et l’absence des design smells comme un ensemble de contraintes et les satisfaire en utilisant Alloy.Automation and intelligence constitute a major preoccupation in the field of software engineering. With the great evolution of Artificial Intelligence, researchers and industry were steered to the use of Machine Learning and Deep Learning models to optimize tasks, automate pipelines, and build intelligent systems. The big capabilities of Artificial Intelligence make it possible to imitate and even outperform human intelligence in some cases as well as to automate manual tasks while rising accuracy, quality, and efficiency. In fact, accomplishing software-related tasks requires specific knowledge and skills. Thanks to the powerful capabilities of Artificial Intelligence, we could infer that expertise from historical experience using machine learning techniques. This would alleviate the burden on software specialists and allow them to focus on valuable tasks. In particular, Model-Driven Engineering is an evolving field that aims to raise the abstraction level of languages and to focus more on domain specificities. This allows shifting the effort put on the implementation and low-level programming to a higher point of view focused on design, architecture, and decision making. Thereby, this will increase the efficiency and productivity of creating applications. For its part, the design of metamodels is a substantial task in Model-Driven Engineering. Accordingly, it is important to maintain a high-level quality of metamodels because they constitute a primary and fundamental artifact. However, the bad design choices as well as the repetitive design modifications, due to the evolution of requirements, could deteriorate the quality of the metamodel. The accumulation of bad design choices and quality degradation could imply negative outcomes in the long term. Thus, refactoring metamodels is a very important task. It aims to improve and maintain good quality characteristics of metamodels such as maintainability, reusability, extendibility, etc. Moreover, the refactoring task of metamodels is complex, especially, when dealing with large designs. Therefore, automating and assisting architects in this task is advantageous since they could focus on more valuable tasks that require human intuition. In this thesis, we propose a cartography of the potential tasks that we could either automate or improve using Artificial Intelligence techniques. Then, we select the metamodeling task and we tackle the problem of metamodel refactoring. We suggest two different approaches: A first approach that consists of using a genetic algorithm to optimize set quality attributes and recommend candidate metamodel refactoring solutions. A second approach based on mathematical logic that consists of defining the specification of an input metamodel, encoding the quality attributes and the absence of smells as a set of constraints and finally satisfying these constraints using Alloy

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems.

Unlike practices in electrical and mechanical equipment engineering, Cyber-Physical Systems (CPS) do not have a set of standardized and harmonized practices for assurance and certification that ensures safe, secure and reliable operation with typical software and hardware architectures. This paper presents a recent initiative called AMASS (Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems) to promote harmonization, reuse and automation of labour-intensive certification-oriented activities via using model-based approaches and incremental techniques. AMASS will develop an integrated and holistic approach, a supporting tool ecosystem and a self-sustainable community for assurance and certification of CPS. The approach will be driven by architectural decisions (fully compatible with standards, e.g. AUTOSAR and IMA), including multiple assurance concerns such as safety, security and reliability. AMASS will support seamless interoperability between assurance/certification and engineering activities along with third-party activities (external assessments, supplier assurance). The ultimate aim is to lower certification costs in face of rapidly changing product features and market needs.This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692474. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Czech Republic, Germany, Sweden, Austria, Italy, United Kingdom, Franc

Model driven product line engineering : core asset and process implications

Reuse is at the heart of major improvements in productivity and quality in Software Engineering. Both Model Driven Engineering (MDE) and Software Product Line Engineering (SPLE) are software development paradigms that promote reuse. Specifically, they promote systematic reuse and a departure from craftsmanship towards an industrialization of the software development process. MDE and SPLE have established their benefits separately. Their combination, here called Model Driven Product Line Engineering (MDPLE), gathers together the advantages of both. Nevertheless, this blending requires MDE to be recasted in SPLE terms. This has implications on both the core assets and the software development process. The challenges are twofold: (i) models become central core assets from which products are obtained and (ii) the software development process needs to cater for the changes that SPLE and MDE introduce. This dissertation proposes a solution to the first challenge following a feature oriented approach, with an emphasis on reuse and early detection of inconsistencies. The second part is dedicated to assembly processes, a clear example of the complexity MDPLE introduces in software development processes. This work advocates for a new discipline inside the general software development process, i.e., the Assembly Plan Management, which raises the abstraction level and increases reuse in such processes. Different case studies illustrate the presented ideas.This work was hosted by the University of the Basque Country (Faculty of Computer Sciences). The author enjoyed a doctoral grant from the Basque Goverment under the “Researchers Training Program” during the years 2005 to 2009. The work was was co-supported by the Spanish Ministry of Education, and the European Social Fund under contracts WAPO (TIN2005-05610) and MODELINE (TIN2008-06507-C02-01)

Engineering Enterprise Software Systems with Interactive UML Models and Aspect-Oriented Middleware

Large scale enterprise software systems are inherently complex and hard to maintain. To deal with this complexity, current mainstream software engineering practices aim at raising the level of abstraction to visual models described in OMG’s UML modeling language. Current UML tools, however, produce static design diagrams for documentation which quickly become out-of-sync with the software, and thus obsolete. To address this issue, current model-driven software development approaches aim at software automation using generators that translate models into code. However, these solutions don’t have a good answer for dealing with legacy source code and the evolution of existing enterprise software systems. This research investigates an alternative solution by making the process of modeling more interactive with a simulator and integrating simulation with the live software system. Such an approach supports model-driven development at a higher-level of abstraction with models without sacrificing the need to drop into a lower-level with code. Additionally, simulation also supports better evolution since the impact of a change to a particular area of existing software can be better understood using simulated “what-if” scenarios. This project proposes such a solution by developing a web-based UML simulator for modeling use cases and sequence diagrams and integrating the simulator with existing applications using aspect-oriented middleware technology

Comparison of method chunks and method fragments for situational method engineering

Two main candidates for the atomic element to be used in Situational Method Engineering (SME) have been proposed: the “method fragment ” and the “method chunk”. These are examined here in terms of their conceptual integrity and in terms of how they may be used in method construction. Also, parallels are drawn between the two approaches. Secondly, the idea of differentiating an interface from a body has been proposed for method chunks (but not for method fragments). This idea is examined and mappings are constructed between the interface and body concepts of method chunks and the concepts used to describe method fragments. The new ISO/IEC 24744 standard metamodel is used as a conceptual framework to perform these mappings

A SPEMOntology for Software Processes Reusing

Reusing the best practices and know-how capitalized from existing software process models is a promising solution to model high quality software processes. This paper presents a part of AoSP (Architecture oriented Software Process) for software processes reuse based on software architectures. The solution is proposed after the study of existing works on software process reusing. AoSP approach deals with the engineering "for" and "by" reusing software processes, it exploits the progress of two research fields that promote reusing in order to improve the software process reusing: domain ontologies and software architectures. AoSP exploits a domain ontology to reuse software process know-how, it allows retrieving, describing and deploring software process architectures. This article details the engineering "for" reusing SPs step of AoSP, it explains how the software process architectures are described and discusses the software process ontology conceptualization and software process knowledge acquisition

Supporting navigation accessibility requirements in Web engineering methods

Web accessibility not only guarantees universal user access to the Web, but also provides interesting benefits for Web development. In order to achieve the goal of Web accessibility, an interesting approach is the incorporation of accessibility requirements into current Web engineering methods. This article presents the Accessibility for Web Applications (AWA) approach with the aim of integrating accessibility into Web engineering methods. The paper also discusses the application of the AWA approach to the Object-Oriented Web Solutions (OOWS) engineering method to produce accessible Web applications with a focus on navigational requirements. In order to demonstrate the practical applicability and usefulness of the approach, a proof of concept is described, the results of which indicating the satisfaction of navigation accessibility requirements. With the application of the AWA approach in the model-driven development (MDD) method, previously-defined OOWS models have been extended with the accessibility criteria, providing resources for the required changes in the process.This study has been developed with the support of the MAVIR Research Network (S2009/TIC-1542 [www.mavir.net/]), MULTIMEDICA PROJECT(tin201020644-c03-01) and the Spanish Ministry of Science and Innovation through the project, PROS-Req TIN2010-19130-C02-02. Co-financing was received from the ERDF.Publicad