Learning to program, and especially understanding it, is a difficult task for newcomers. For this rea- son, aids are provided, such as IDEs, which give them tools to help them avoid syntax and/or semantic errors, depending on the programming languages used.
This dissertation explores the impact of using natural language in the visualisation of open datasets, focusing on the design and evaluation of Aladdin, a system based on the DSR approach. Aladdin uses advanced natural language processing techniques to transform text queries into interactive data visualisations.
This master thesis investigates how the Language Server Protocol (LSP) can be used to develop a nomad and ergonomic code editor. Mobile devices popularity has significantly increased in the past decade, strengthening the transformation of desktop solutions to mobile ones.
Since many years, Odoo, a company providing business management services, is constantly expanding its scope and developing the complexity of its software, a web application. In response to that complexity, the introduction of automated testing techniques seems to be the next evolution of the testing tools already available to them.
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.
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.