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An Empirical Study on Clustering for Isolating Bugs in Fault Localization

An Empirical Study on Clustering for Isolating Bugs in Fault Localization. Yanqin Huang 1 , Junhua Wu 2 , Yang Feng 1 , Zhenyu Chen 1 , Zhihong Zhao 1. Outline. Introduction Assumption Our Approach Experimental Design Experimental Results Conclusion. Introduction.

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An Empirical Study on Clustering for Isolating Bugs in Fault Localization

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  1. An Empirical Study on Clustering for Isolating Bugs in Fault Localization Yanqin Huang1, Junhua Wu2, Yang Feng1, Zhenyu Chen1, Zhihong Zhao1

  2. Outline • Introduction • Assumption • Our Approach • Experimental Design • Experimental Results • Conclusion

  3. Introduction • SBFL: Spectrum-Based Fault Location • program spectrum • risk evaluation formulas • Clustering: group tests

  4. Assumption • negligible effect of multiple faults • test cases have some similar behaviors if they due to the same fault

  5. Our Approach • SBFL Concept Figure: An example for SBFL

  6. Our Approach • Current SBFL Techniques Table: An example for SBFL

  7. Our Approach • Cluster Algorithms • Hierarchical clustering Algorithms • Partitioning clustering Algorithms e.g: simple k-means clustering

  8. Our Approach • Framework • Executing Test Cases • Collecting Profiles • Extracting BugHit Matrix • Applying Clustering • Using SBFL for Fault Localization

  9. Experimental Design • Subject Programs

  10. Experimental Design

  11. Experimental Design • Experimental Steps • Inject faults • Distinguish failures • Get execution profile • Cluster • Locate faults

  12. Experimental Results • Experimental Results on the flex program

  13. Experimental Results • Experimental Results on the grep program

  14. Experimental Results • Observations • simple k-means clustering mostly performs better than hierarchical clustering • Wong1 picked from ER5 always has the most excellent performance comparing to other techniques

  15. Experimental Results • Comparison of Wong1 from ER5 with Clustering Algorithms

  16. Experimental Results • Comparison of Wong1 and Naish2

  17. Conclusion • Conclusion • ER5 (Wong1) achieves the best results of fault localization with clustering • K-means outperforms hierarchical clustering for isolating bugs in fault localization

  18. Thank you!

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