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.
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.
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.
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.
This master thesis project, revisits the links between search-based crash reproduction and software quality metrics to assess the hardness of search-based crash reproducing test case generation.
Flaky tests are nondeterministic tests, they can give different results without changes to the code. This wastes time and resources. A better understanding of this field should lead to a decrease of these inconveniences. However, there is little work that makes the effort to bring this knowledge together. This is why this thesis will propose to answer these questions What are the categories of flaky tests identified? and What debugging techniques are the most appropriate for the different categories of flaky tests?
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.