Vijay Pahuja and Vishal Padh, Technical Debt, AI, Software Engineering, Development Tools United States of America
Technical Debt is one of the biggest issues hindering the digital transformation of organizations. Cost of addressing debt has been rising. AI powered tools can overcome problems of traditional tools as they continuously learn and adapt new patterns. They can proactively detect issues, suggest refactoring, and provide insight to areas of improvement in the codebase, pushing for more sustainable software development practices. While AI offers tremendous potential for managing and reducing technical debt, AI based tools come with their own challenges as AI is heavily dependent on the quality and quantity of data on which they are trained. As organizations rely more and more on AI, they may end up with monotonous codebases producing mediocre products as use of AI will lead to skill degradation and affect critical thinking.
Technical Debt, AI, Software Engineering, Development Tools.
Xia Li, Tanvi Mistry, The Department of Software Engineering and Game Design and Development, Kennesaw State University, Marietta, USA
Version control systems (VCS) play a crucial role by enabling developers to record changes, revert to previous versions, and coordinate work across distributed teams. In version control systems (e.g., GitHub), commit message serves as concise descriptions of code changes made during development. In our study, we evaluate the performance of multi-label commit message classification using p-tuning (learnable prompt templates) through three pre-trained models such as BERT, RoBERTa and DistilBERT. The experimental results demonstrate that RoBERTa model outperforms other two models in terms of the widely used evaluation metrics (e.g., achieving 81.99% F1 score).
Multi-label commit message classification, p-tuning, pre-trained models.
