In recent years, GitHub Actions (GHA) has emerged as the leading platform for Continuous Integration and Continuous Deployment (CI/CD) within the GitHub ecosystem, offering developers seamless workflow automation. However, as with other CI/CD tools, GHA workflows are susceptible to ”smells” which are suboptimal practices that can lead to technical debt, reduced maintainability, and performance issues. This thesis investigates the prevalence and nature of these workflow smells in GHA configurations. Through an extensive analysis of commit histories from 83 projects, we identify common patterns of frequent changes in GHA workflows that may indicate the presence of smells. We propose a set of potential GHA-specific smells, develop a tool to automatically detect these smells, and validate our findings through a contribution study involving 40 pull requests to open-source projects. After qualitatively analysing the comments on 32 pull requests we settle on 7 confirmed GHA workflows smells, including one novel smell previously unrecognised in the literature, This work contributes to improving the quality of GHA workflows and offers insights for developers to optimise their CI/CD processes. Finally, this research was also accepted as a paper to the SCAM 2024 conference.

Rotterdam Werkt! is a network of fourteen organizations in the Rotterdam area in the Netherlands. Their goal is to increase labor mobility between these organizations through sharing vacancies, exchanging employees and partaking in joint projects. Rotterdam Werkt! has tasked us with creating a central platform on which all vacancies are automatically combined from the websites of all the organizations in the network. The two main challenges of the project were to gather the vacancies from all the organizations affiliated with Rotterdam Werkt! and allow their recruiters to search and filter through them. This meant that a significant amount of research needed to be done in order to find a suitable scraping tool as well as a suitable search engine. Whilst gathering the vacancies, we ran into the problem that each website was significantly different in the way it is rendered. Furthermore, we also needed to categorize the data correctly such that it becomes searchable in the search engine. Lastly, the retrieval function needed to be optimized such that the most relevant vacancies would be returned for a given query. In order to assess whether recruiters could use the search engine effectively in practice, an evaluation of the effectiveness of the search engine was done. Three retrieval functions were compared based on a significance test of several effectiveness measures that indicate to what extent a retrieval function is able to retrieve relevant documents, or in this case, vacancies. Out of the three, the retrieval function that scored the highest was chosen to be used in the platform, so that recruiters will have a bigger chance to find the vacancies they will be looking for. In the end, we consider our project to be a success. We managed to scrape all vacancies from all the websites of the organizations in Rotterdam Werkt! and to combine these on a centralized platform. Furthermore, the search engine evaluation allowed us to select the best vacancy retrieval function out of the three evaluated retrieval functions. However, more work can still be put into evaluating the search engine in the future by testing more retrieval functions based on more vacancy data, so that the search functionality can be further improved.