This project focuses on developing AI-driven predictive models to analyze historical defects, releases' data, and codebases for the identification of code areas prone to defects in future software releases.
This project aims to develop and validate AI models capable of:
- Analyzing historical defects, releases and codebase data to identify patterns linked to defect-prone areas.
- Predicting code components at risk of defects in future releases based on planned changes, feature scope, and historical defect patterns
This is an opportunity to contribute to impactful research that addresses one of the most critical issues in software quality, a topic that is relevant to the software industry.
At CoSELab and in collaboration with three Danish companies, you will work at the intersection of AI and software engineering, gaining hands-on experience with machine learning algorithms, data analytics, and predictive modeling. Contributing to cutting-edge research with industry impact, you will develop highly sought-after skills in both academia and industry.
Preferred student profile
Software Engineering / Computer Science / Artificial Intelligence / Data Science / Machine Learning
Computer Science / Software Engineering with Machine Learning skills.
Responsible researcher
Associate Professor Adam Alami
Assistant Professor Abhishek Tiwari
Maximum number of students to host
2
Dates
This program has a possible flexible start and length.