Selecting discriminating terms for bug assignment: A formal analysis

Ibrahim Aljarah, Shadi Banitaan, Sameer Abufardeh, Wei Jin, Saeed Salem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Background. The bug assignment problem is the problem of triaging new bug reports to the most qualified developer. The qualified developer is the one who has enough knowledge in a specific area that is relevant to the reported bug. In recent years, bug triaging has received a considerable amount of attention from researchers. In previous work, bugs were represented as vectors of terms extracted from the bug reports' description. Once the bugs are represented as vectors in the terms space, traditional machine learning techniques are employed for the bug assignment. Most of the previous algorithms are marred by low accuracy values. Aims. This paper formulates the bug assignment problem as a classification task, and then examines the impact of several term selection approaches on the classification effectiveness. Method. Three variants selection methods that are based on the Log Odds Ratio (LOR) score are compared against methods that are based on the Information Gain (IG) score and Latent Semantic Analysis (LSA). The main difference in the methods that are based on the LOR score is in the process of selecting the terms. Results. Term selection techniques that are based on the Log Odds Ratio achieved up to 30% improvement in the precision and up to 5% higher in recall compared to other term selection methods such as Latent Semantic Analysis and Information Gain. Conclusions. Experimental results showed that the effectiveness of bug assignment methods is directly affected by the selected terms that are used in the classification methods.

Original languageEnglish (US)
Title of host publicationPROMISE 2011 - 7th International Conference on Predictive Models in Software Engineering, Co-located with ESEM 2011
DOIs
StatePublished - Oct 19 2011
Externally publishedYes
Event7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011 - Banff, AB, Canada
Duration: Sep 20 2011Sep 21 2011

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011
CountryCanada
CityBanff, AB
Period9/20/119/21/11

Keywords

  • Bug assignment
  • Bug reports
  • Classification
  • Machine learning

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