Automated data collection and dashboard development : enhancing quality monitoring and analysis

This master thesis focuses on optimizing software quality analysis at target projects in Elektrobit, a key player in the automotive software industry. In the light of continuous technological advancements in the industry, maintaining high-quality software is imperative. Current verification procedures, though effective, are time-consuming and often entail a significant amount of manual work.

Additionally, the absence of efficient visualization tools leads to missed opportunities for improvement. I proposed an automated quality data collection process and a user-friendly visualization system, designed to streamline the process of software quality analysis. The solution is delivered in the form of an interactive dashboard, offering a centralized view of all pertinent quality metrics data, thereby saving time, reducing manual work, and improving user experience.

The study employs a design science research (DSR) methodology, focusing on iterative design, implementation, and evaluation. The findings from the evaluation confirm the dashboard’s efficacy and relevance, illustrating its value in enhancing efficiency, productivity, and team collaboration. The user-centered design approach and regular user engagement during development proved to be instrumental in achieving these results.

The research acknowledges certain limitations, such as the proof-of-concept stage of the dashboard and the relatively small user feedback group and suggests potential areas for future work. In conclusion, this research serves as a testament to the transformative potential of automated data collection and effective visualization systems in the software quality analysis domain, contributing to both theory and practice.