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PROMISE 2011: "Detecting Bug Duplicate Reports through Locality of Reference"

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CS, NcState
CS, NcState

PROMISE 2011: "Detecting Bug Duplicate Reports through Locality of Reference" Tomi Prifti, Sean Banerjee and Bojan Cukic.

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Detecting Bug Duplicate Reports Through Locality of Reference Tomi Prifti, Sean Banerjee, Bojan Cukic Lane Department of CSEE West Virginia University Morgantown, WV, USA September 2011
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A typical bug report
Goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Firefox Repository ,[object Object],[object Object],[object Object]
Time Interval Between Reports ,[object Object]
Experimental Setup ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Procedure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measuring Performance ,[object Object],[object Object],[object Object]
Approach methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sliding Window Defined ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Results ,[object Object]
Performance and Runtime ,[object Object],[object Object]
Result Comparisons Group Approach Results Hiew, et-al Text analysis Recall rate ~50% Cubranic, et-al Bayesian learning Text categorization Correctly predicted ~30% duplicates Jalbert, et-al Text Similarity Clustering Recall rate ~51% List size 20 Wang, et-al NLP Execution Information 67-93% detection rate (43-72% with NLP) Wang, et-al Enhanced version of prior algorithm 17-31% improvement over state of art Our approach Time Window/Centroids ~53% recall rate
Threats to Validity ,[object Object],[object Object],[object Object]
Summary and Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TF/IDF ,[object Object],[object Object],[object Object],[object Object],[object Object]
Sliding Window - TF/IDF ,[object Object],[object Object],[object Object],[object Object]
Sliding Window – Centroid ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sliding Window – Centroid – TD/IDF ,[object Object],[object Object]

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PROMISE 2011: "Detecting Bug Duplicate Reports through Locality of Reference"

  • 1. Detecting Bug Duplicate Reports Through Locality of Reference Tomi Prifti, Sean Banerjee, Bojan Cukic Lane Department of CSEE West Virginia University Morgantown, WV, USA September 2011
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  • 18. Result Comparisons Group Approach Results Hiew, et-al Text analysis Recall rate ~50% Cubranic, et-al Bayesian learning Text categorization Correctly predicted ~30% duplicates Jalbert, et-al Text Similarity Clustering Recall rate ~51% List size 20 Wang, et-al NLP Execution Information 67-93% detection rate (43-72% with NLP) Wang, et-al Enhanced version of prior algorithm 17-31% improvement over state of art Our approach Time Window/Centroids ~53% recall rate
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