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Improving the effectiveness of test suite through mining historical data

Published:31 May 2014Publication History

ABSTRACT

Software regression testing is an integral part of most major software projects. As projects grow larger and the number of tests increases, performing regression testing becomes more costly. If software engineers can identify and run tests that are more likely to detect failures during regression testing, they may be able to better manage their regression testing activities. In this paper, to help identify such test cases, we developed techniques that utilizes various types of information in software repositories. To assess our techniques, we conducted an empirical study using an industrial software product, Microsoft Dynamics AX, which contains real faults. Our results show that the proposed techniques can be effective in identifying test cases that are likely to detect failures.

References

  1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, pages 487–499, Sept. 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. R. V. Binder. Testing Object-Oriented Systems. Addison Wesley, Upper Saddle River, NJ, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. H. Do, S. Mirarab, L. Tahvildari, and G. Rothermel. The effects of time constraints on test case prioritization: A series of controlled experiments. IEEE Transactions on Software Engineering, 26(5), Sept. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. H. Do and G. Rothermel. An empirical study of regression testing techniques incorporating context and lifecycle factors and improved cost-benefit models. In Proceedings of the ACM SIGSOFT Symposium on Foundations of Software Engineering, Nov. 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. E. Engstrom, P. Runeson, and M. Skoglund. A systematic review on regression test selection techniques. Information and Software Technology, 52(1):14 – 30, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Han, J. Pei, Y. Yin, and R. Mao. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. In Data Mining and Knowledge Discovery, pages 53–87, Jan. 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. B. Kaner and pettichord. Lessons Learned in Software Testing. Wiley Computer Publishing, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Korel. The program dependence graph in static program testing. In Information Processing Letters, volume 24, pages 103–108, 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. B. Livshits and T. Zimmermann. DataMine: Finding common error patterns by mining software revision histories. In International Symposium on Foundations of Software Engineering, pages 296–305, Sept. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Microsoft Corporation. XML Documentation Tags. http://msdn.microsoft.com/en-us/library/cc607340.aspx, Feb. 2010.Google ScholarGoogle Scholar
  11. N. Nagappan and T. Ball. Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study. In International Symposium on Empirical Software Engineering and Measurement, pages 364–373, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. N. Nagappan, T. Ball, and A. Zeller. Mining metrics to predict component failures. In Proceedings of the International Conference on Software Engineering, May 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Yoo and M. Harman. Regression testing minimisation, selection and prioritisation : A survey. Software Testing, Verification, and Reliability, Mar. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Zaki and K. Gouda. Fast vertical mining using diffsets. In Proceedings of the ningth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 326–335, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Zimmermann and N. Nagappan. Predicting defects using network analysis on dependency graphs. In Proceedings of the 30th International Conference on Software Engineering, pages 531–540, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Zimmermann, P. Weibgerber, S. Diehl, and A. Zeller. Mining versions histories to guide software changes. In Proceedings of the International Conference on Software Engineering, pages 563–572, May 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories
      May 2014
      427 pages
      ISBN:9781450328630
      DOI:10.1145/2597073
      • General Chair:
      • Premkumar Devanbu,
      • Program Chairs:
      • Sung Kim,
      • Martin Pinzger

      Copyright © 2014 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 May 2014

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