SCA: a semantic conflict analyzer for parallel changes

  • Authors:

  • Danhua Shao;Sarfraz Khurshid;Dewayne E. Perry

  • Affiliations:

  • The University of Texas at Austin, Austin, TX, USA;The University of Texas at Austin, Austin, TX, USA;The University of Texas at Austin, Austin, TX, USA

  • Venue:
  • Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
  • Year:
  • 2009

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Abstract

Parallel changes are becoming increasingly prevalent in the development of large scale software system. To further study the relationship between parallel changes and faults, we have designed and implemented a semantic conflict analyzer (SCA) to detect semantic interference between parallel changes. SCA combines data dependency analysis and program slicing. Data dependency analysis can disclose the semantic structure of the program. And program slicing can identify which semantic structures are impacted by a change. By comparing the overlap between impacts of two changes, SCA can detect if there are semantic interference between the two changes. An experiment with an industrial project shows that SCA can detect a significant portion of the faults in highly parallel changes. SCA is effective in predicting faults (based on "direct" semantic interference detection) in changes made within a short time period. SCA is both efficient (averaging less than two minutes) and scalable (requiring only the local context)