Analysis of code «hotspots» in relational repositories of intelligent transportation system software

Abstract

Aim. Developing a reproducible methodology for identifying source code «hotspots» based on Git data to better understand development history, identifying flawed areas and architectural deficiencies.  Methods. The study employs relational analysis to enable end-to-end analytics of development history by linking the entities File → Commit → Pull Request → Issue. Three key criteria are used: file change frequency, number of contributors, and the number of bugfix changes.  Results. The methodology was tested on real API service data from two intelligent transportation systems. Ranked lists for each criterion were obtained along with their association with the system’s functional components.  Conclusion. An analysis of a relational repository provides valuable insights into development history, helps identify code areas requiring greater attention, and thereby improves software quality.