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Identifying key modules in evolving object-oriented software systems is crucial. A network-based approach reveals that centrality measures like CIRank can effectively predict the scope of change propagation, aiding software quality improvement.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Network Analysis

Background:

  • Object-oriented software systems undergo continuous evolution to meet changing requirements.
  • Understanding change characteristics is vital for software quality improvement by testers and designers.
  • Identifying critical modules is essential for managing software evolution effectively.

Purpose of the Study:

  • To propose a novel network-based approach for analyzing change distributions in software evolution.
  • To investigate the correlation between network centrality measures and the scope of change propagation.
  • To identify reliable indicators for predicting the impact of changes in object-oriented systems.

Main Methods:

  • Construction of software dependency networks at the class level.
  • Analysis of co-change frequencies among classes from software repositories.
  • Calculation of the scope of change propagation based on dependencies and co-changes.
  • Application of Spearman rank correlation to analyze the relationship between centrality measures and change propagation scope.
  • Conducting case studies on open-source Java projects (Findbugs, Hibernate, Spring).

Main Results:

  • Change distribution within software systems is highly uneven.
  • Centrality measures including PageRank, Degree, and CIRank demonstrate significant correlation with the scope of change propagation.
  • CIRank exhibits a higher correlation coefficient compared to other measures, indicating its superior predictive power.

Conclusions:

  • The proposed network-based approach effectively analyzes change propagation in object-oriented software.
  • Centrality measures, particularly CIRank, serve as valuable indicators for assessing the potential impact of code changes.
  • Findings support informed decision-making in software maintenance and evolution for improved quality.