Msc Thesis

L’application de tests automatiques au code soumis par les étudiants sur une plateforme de correction automatique est un outil utile pour le corps enseignant. Il permet de fournir de meilleurs retours, sur plus d’exercices, créés plus rapidement. Des méthodes des tests automatiques sont analysées et une sélection est faite sur ceux qui, de par leurs caractéristiques, sont les plus intéressants dans le contexte de la correction automatique des codes d’étudiants. Les méthodes retenues sont le fuzzing en boite grise et le test de combinaisons d’appels sur une structure donnée. L’efficacité de celles-ci est discutée et une application pratique est développée sous la forme d’une librairie de test qui s’intègre dans la plateforme de correction automatique Inginious. Les limitations sont analysées et un protocole de test des modifications apportées par celles-ci est ensuite proposé pour pouvoir quantifier les gains apportés via une expérience pratique.

Learning software testing is a neglected subject in computer science courses. Over the years, methods and tools have appeared to provide educational support for this learning. Mutation testing is a technique used to evaluate the effectiveness of test suites. Recently, a variant called extreme mutation testing that reduces computational and time costs has emerged. Descartes, an extreme mutation engine was developed. With the support of a plugin extension called Reneri, Descartes can generate a report providing information to the developer on potential reasons why mutants remain undetected. In this thesis, an extension of Visual Studio Code has been developed in order to incorporate the information generated by Descartes and Reneri. The purpose of the experiment is to assess whether the inclusion of this data can help master’s students improve their test assertions. The results showed that this information integrated into an editor was well received by the students and that it guided them towards a refinement of their suite of tests.

Although software testing is critical in software engineering, studies have shown a significant gap between students’ knowledge of software testing and the industry’s needs, hinting at the need to explore novel approaches to teach software testing. Among them, classical mutation testing has already proven to be effective in helping students. We hypothesise that extreme mutation testing could be more effective by introducing more obvious mutants to kill. In order to study this question, we organised an experiment with two undergraduate classes comparing the usage of two tools, one applying classical mutation testing, and the other one applying extreme mutation testing. The results contradicted our hypothesis. Indeed, students with access to the classic mutation testing tool obtained a better mutation score, while the others seem to have mostly covered more code. Finally, we have published and anonymised the students’ test suites in adherence to best open-science practices, and we have developed guidance based on previous evaluations and our own results.

Reverse Engineering a Feature Model (FM) of an existing system allows its migration to a software product line approach to simplify the management of this system by applying a Software Product Line Engineering methodology that focuses mainly on the FM to determine the reusable artefacts and the variation points of the system. This thesis is a case study on the Odoo framework to define a reverse engineering approach that can drive an automatic synthesis of an FM to represent the variability architecture of the system. We manually explored the Odoo framework source code to identify variability patterns, then exploited Formal Concept Analysis properties to derive the FM based on the Odoo module’s dependencies. The heuristic that we executed for the process of reverse engineering is effective and results in FM, which describes the product configuration variability.

Many applications are developed with a lot of different purposes and can provide quality output. Nevertheless, crashes still happen. Many techniques such as unit testing, peer-reviewing, or crash reproduction are being researched to improve quality by reducing crashes. This thesis contributes to the fast-evolving field of research on crash reproduction tools. These tools seek better reproduction with minimum information as input while delivering correct outputs in various scenarios. Different approaches have previously been tested to gather input-output data, also called benchmarks, but they often take time and manual e↵ort to be usable. The research documented in this thesis endeavours to synthesize crashes using mutation testing to serve as input for crash reproduction tools.