  Context: In the realm of software development, maintaining high software
quality is a persistent challenge. However, this challenge is often impeded by
the lack of comprehensive understanding of how specific code modifications
influence quality metrics.
  Objective: This study ventures to bridge this gap through an approach that
aspires to assess and interpret the impact of code modifications. The
underlying hypothesis posits that code modifications inducing similar changes
in software quality metrics can be grouped into distinct clusters, which can be
effectively described using an AI language model, thus providing a simple
understanding of code changes and their quality implications.
  Method: To validate this hypothesis, we built and analyzed a dataset from
popular GitHub repositories, segmented into individual code modifications. Each
project was evaluated against software quality metrics pre and
post-application. Machine learning techniques were utilized to cluster these
modifications based on the induced changes in the metrics. Simultaneously, an
AI language model was employed to generate descriptions of each modification's
function.
  Results: The results reveal distinct clusters of code modifications, each
accompanied by a concise description, revealing their collective impact on
software quality metrics.
  Conclusions: The findings suggest that this research is a significant step
towards a comprehensive understanding of the complex relationship between code
changes and software quality, which has the potential to transform software
maintenance strategies and enable the development of more accurate quality
prediction models.
