The field of automated test case generation has grown considerably in recent years to reduce software testing costs and find bugs. However, the techniques for automatically generating test cases for machine learning libraries still produce low-quality tests and papers on the subject tend to work in Java, whereas the machine learning community tends to work in Python.
The widespread digitalization of society and the increasing complexity of software make it essential to develop high-quality software testing suites. In recent years, several techniques for learning software testing have been developed, including techniques based on mutation testing.
Energy efficiency in computing is an important subject that is increasingly being addressed by researchers and developers. Nowadays, the majority of websites are built using the Wordpress CMS, while other developers prefer to use more secure and energy-efficient site generators.
Software development faces persistent challenges in terms of maintainability and efficiency, and this is driving the ongoing search for innovative approaches. Agile methodologies, in particular Behaviour-Driven Development (BDD), have gained ground in society thanks to their ability to promote responsiveness to change and communication between stakeholders.
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.