Conference Proceedings

Combining Query Reduction and Expansion for Text-Retrieval-Based Bug Localization

Juan Manuel Florez, Oscar Chaparro, Christoph Treude, Andrian Marcus

2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2021) | IEEE COMPUTER SOC | Published : 2021

Abstract

Automated text-retrieval-based bug localization (TRBL) techniques normally use the full text of a bug report to formulate a query and retrieve parts of the code that are buggy. Previous research has shown that reducing the size of the query increases the effectiveness of TRBL. On the other hand, researchers also found improvements when expanding the query (i.e., adding more terms). In this paper, we bring these two views together to reformulate queries for TRBL. Specifically, we improve discourse-based query reduction strategies, by adopting a combinatorial approach and using task phrases from bug reports, and combine them with a state-of-the-art query expansion technique, resulting in 970 q..

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Grants

Awarded by National Science Foundation


Funding Acknowledgements

This research was supported in part by grants from the National Science Foundation: CCF-1848608, CCF-1910976, CCF-1955837, and CCF-1955853.