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Multiple techniques exist to find vulnerabilities in code, such as static analysis and machine learning. Although machine learning techniques are promising, they need to learn from a large quantity of examples. Since there is not such large quantity of data for vulnerable code, vulnerability injection techniques have been developed to create them. Both vulnerability prediction and injection techniques based on machine learning usually use the same kind of data, thus pairs of vulnerable code, just before the fix, and their fixed version. However, using the fixed version is not realistic, as the vulnerability has been introduced on a different version of the code that may be way different from the fixed version. Therefore, we suggest the use of pairs of code that has introduced the vulnerability and its previous version. Indeed, this is more realistic, but this is only relevant if machine learning techniques can properly learn from it and the patterns learned are significantly different than with the usual method. To make sure of this, we trained vulnerability prediction models for both kind of data and compared their performance. Our analysis showed a model trained on pairs of vulnerable code and their fixed version is unable to predict vulnerabilities from the vulnerability introducing versions. The same goes for the opposite, despite both models are able to properly learn from their data and detect vulnerabilities on similar data. Therefore, we conclude that the use of vulnerability introducing codes for machine learning training is more relevant than the fixed versions.

This thesis explores the idea of applying genetic improvement in the aim of injecting vulnerabilities into programs. Generating vulnerabilities automatically in this manner would allow creating datasets of vulnerable programs. This would, in turn, help training machine-learning models to detect vulnerabilities more efficiently. This idea was put to the test by implementing VulGr, a modified version of the framework dedicated to genetic improvement named PyGGi. VulGr itself uses CodeQL, a static code analyser, offering a new approach to statical detection of vulnerabilities. VulGr’s end goal was to use CodeQL to inject vulnerabilities into programs of the Vul4J dataset. This experiment proved unsuccessful, CodeQL lacking accuracy and being too time-consuming to produce concrete results in an acceptable time span (less than 72 hours). However, the general approach and VulGr still retain their relevancy for future uses as CodeQL is an ongoing community effort promising new updates fixing the issues mentioned.

Application Programming Interfaces, known as APIs, are increasingly popular in modern web applications. With APIs, users around the world are able to access a plethora of data contained in numerous server databases. To understand the workings of an API, a formal documentation is required. This documentation is also required by API testing tools, aimed at improving the reliability of APIs. However, as writing API documentations can be time-consuming, API developers often overlook the process, resulting in unavailable, incomplete or informal API documentations. Recent Large Language Model technologies such as ChatGPT have displayed exceptionally efficient capabilities at automating tasks, disposing of data trained on billions of resources across the web. Thus, such capabilities could be utilized for the purpose of generating API documentations. Therefore, the Master’s Thesis proposes the first approach Leveraging Large Language Models to Automatically Infer RESTful API Specifications. Preliminary strategies are explored, leading to the implementation of a tool entitled MutGPT. The intent of MutGPT is to discover API features by generating and modifying valid API requests, with the help of Large Language Models. Experimental results demonstrate that MutGPT is capable of sufficiently inferring the specification of the tested APIs, with an average route discovery rate of 82.49% and an average parameter discovery rate of 75.10%. Additionally, MutGPT was capable of discovering 2 undocumented and valid routes of a tested API, which has been confirmed by the relevant developers. Overall, this Master’s Thesis uncovers 2 new contributions:

Code Smells have been studied for more than 20 years now. They are used to describe a design 􏰊aw in a program intuitively. In this study, we wish to identify the impact of some of these Code Smells. And, more specifically, their potential impact on Testability. To do this, we will study the state of the research on both Code Smells and Testability. Using those studies, we will define a scope of parameters to de􏰉ne the two concepts. With that information, we will analyse the statistical distribution of our samples and try to understand the relationship between Code Smells and Testability in a corpus of Java projects.

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.