Investigating Code Reuse in Software Redesign: A Case Study

2026-04-09

AI Summary

This study addresses the challenges of high cost, error-proneness, and defect propagation in cross-repository code and test reuse during software refactoring. Through action research, the authors conduct bidirectional empirical analyses on real-world cases such as Soot/SootUp and FindBugs/SpotBugs, identifying for the first time the bidirectional reuse requirements and semantic reuse patterns inherent in refactoring scenarios. They propose a semantic alignment–based code mapping approach coupled with a hierarchical, extensible clone detection mechanism. Experimental results demonstrate that their method reduces irrelevant clones by 33%–99% on average and achieves a benchmark precision of 86%. The practical impact is further evidenced by five reported issues and ten pull requests submitted to open-source communities, eight of which have already been merged, confirming the approach’s effectiveness and applicability.

Abstract

Software redesign preserves functionality while improving quality attributes, but manual reuse of code and tests is costly and error-prone, especially in crossrepository redesigns. Focusing on static analyzers where cross-repo redesign needs often arise, we conduct a bidirectional study of the ongoing Soot/SootUp redesign case using an action research methodology that combines empirical investigation with validated open-source contributions. Our study reveals: (1) non-linear migration which necessitates bidirectional reuse, (2) deferred reuse via TODOs, (3) neglected test porting, and (4) residual bug propagation during migrations. We identify tracking corresponding code and tests as the key challenge, and address it by retrofitting clone detection to derive code mappings between original and redesigned projects. Guided by semantic reuse patterns derived in our study, we propose Semantic Alignment Heuristics and a scalable hierarchical detection strategy. Evaluations on two redesigned project pairs (Soot/SootUp and FindBugs/SpotBugs) show that our approach achieves an average reduction of 33-99% in likely irrelevant clones at a SAS threshold of 0.5 across all tool results, and improves precision up to 86% on our benchmark of 1,749 samples. Moreover, we contribute to the redesigned projects by submitting five issues and 10 pull requests, of which eight have been merged.

Problem

• code reuse
• software redesign
• cross-repository migration
• test porting
• clone detection

Innovation

• clone detection
• semantic alignment heuristics
• code migration
• software redesign
• cross-repository reuse