Change-Patterns Mapping: A Boosting Way for Change Impact Analysis

Yuan Huang, Jinyu Jiang, Xiapu Luo, Xiangping Chen, Zibin Zheng, Nan Jia, Gang Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


Change impact analysis (CIA) is a specialized process of program comprehension that investigates the ripple effects of a code change in a software system. In this paper, we present a boosting way for change impact analysis via mapping the historical change-patterns to current CIA task in a cross-project scenario. The change-patterns reflect the coupling dependencies between changed entities in a change set. A traditional CIA tool (such as ImpactMiner) outputs an initial impact set for a starting entity. To boost the traditional CIA tool, our approach retrieves an equivalent entity from various historical change sets for the starting entity. Then, the change-patterns between the equivalent entity and the rest of entities in the change set are mapped to the CIA task at hand. For current CIA task, if an entity in the initial impact set involves the similar change-pattern with the starting entity when comparing with the mapped change-pattern, we will reward the impacted confidence of the entity. Accuracy improvements are observed in the experiments when applying our boosting method to three famous CIA tools, i.e., ImpactMiner, JRipples and ROSE.

Original languageEnglish
Pages (from-to)1-21
JournalIEEE Transactions on Software Engineering
Issue number1
Publication statusPublished - 16 Feb 2021


  • Boosting
  • Boosting Method
  • Change Impact Analysis
  • Change-Patterns
  • Coupling Dependency
  • Couplings
  • Licenses
  • Software
  • Software systems
  • Task analysis
  • Tools

ASJC Scopus subject areas

  • Software

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