Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive. Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer’s profile, form a balanced team, support code review, etc. However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement. To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions. First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes. Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples. We also studied the sensitivity of change classification of various types of tokens present in commit messages. The experimental outcomes employing 10-fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%. The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification

Release notes are admitted as an essential document by practitioners. They contain the summary of the source code changes for the software releases, such as issue fixes, added new features, and performance improvements. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1,900 releases, among them 37% of the release notes are empty. We propose an automatic generate release notes approach based on the commit messages and merge pull-request (PR) titles to mitigate this problem. We implement one of the popular extractive text summarization techniques, i.e., the TextRank algorithm. However, accurate keyword extraction is a vital issue in text processing. The keyword matching and topic extraction process of the TextRank algorithm ignores the semantic similarity among texts. To improve the keyword extraction method, we integrate the GloVe word embedding technique with TextRank. We develop a dataset with 1,213 release notes (after null filtering) and evaluate the generated release notes through the ROUGE metric and human evaluation. We also compare the performance of our technique with another popular extractive algorithm, latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms LSA.