Lightweight Context-Aware Evaluation of Commit Messages Using Semantic Diff Alignment

This work discusses shortcomings in the current methods of evaluating commit messages that focus on syntactic and surface readability metrics. The suggested structure presents a diff-semantic alignment tool that transforms the code changes to structured textual summaries to be compared with the commit messages. TF-IDF representation and n-gram analysis are used to extract textual features and estimate semantic alignment with the help of the computation of cosine similarity. To classify commit message quality using a combination of linguistic and alignment features a lightweight classification layer is used, which is based on the Logistic Regression. It does not rely on abstract syntax tree extraction or deep learning architectures, making it easier to compute and integrate into development processes. It is evaluated using a dataset of 13,200 commits in a sample of a dozen open repositories in various fields of programming on GitHub. Baseline techniques are rule-based readability scores and AST-based semantic analysis techniques that have appeared in the literature. Findings indicate that the suggested framework obtains 86.7% alignment accuracy, which is 12.4% higher on average than the baseline techniques. The system has an average processing latency of 36 milliseconds per commit and uses 39% less computation than deep learning-based models. These findings support the claim that text based semantic alignment is an effective and scalable tool to enhance the quality of commit messages in continuous integration setting.