research-article

Selecting discriminating terms for bug assignment: a formal analysis

Authors:

Ibrahim Aljarah,

Shadi Banitaan,

Sameer Abufardeh,

Wei Jin,

Saeed SalemAuthors Info & Claims

Promise '11: Proceedings of the 7th International Conference on Predictive Models in Software Engineering

Article No.: 12, Pages 1 - 7

https://doi.org/10.1145/2020390.2020402

Published: 20 September 2011 Publication History

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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.

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Qamar KSülün ETüzün E(2022)Taxonomy of bug tracking process smellsInformation and Software Technology10.1016/j.infsof.2022.106972150:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.106972
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Selecting discriminating terms for bug assignment: a formal analysis

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Abstract
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Published In

Promise '11: Proceedings of the 7th International Conference on Predictive Models in Software Engineering

September 2011

145 pages

ISBN:9781450307093

DOI:10.1145/2020390

General Chair:
Tim Menzies
WVU

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 20 September 2011

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Promise '11

Promise '11: 7th International Conference on Predictive Models in Software Engineering

September 20 - 21, 2011

Alberta, Banff, Canada

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Promise '11 Paper Acceptance Rate 15 of 35 submissions, 43%;

Overall Acceptance Rate 98 of 213 submissions, 46%

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Qamar KSülün ETüzün E(2022)Taxonomy of bug tracking process smellsInformation and Software Technology10.1016/j.infsof.2022.106972150:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.106972
Goyal ASardana N(2021)Analytical Study on Bug Triaging PracticesResearch Anthology on Recent Trends, Tools, and Implications of Computer Programming10.4018/978-1-7998-3016-0.ch049(1068-1094)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3016-0.ch049
Oliveira PAndrade RBarreto INogueira TMorais Bueno L(2021)Issue Auto-Assignment in Software Projects with Machine Learning Techniques2021 IEEE/ACM 8th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)10.1109/SER-IP52554.2021.00018(65-72)Online publication date: Jun-2021
https://doi.org/10.1109/SER-IP52554.2021.00018
Qamar KSulun ETuzun E(2021)Towards a Taxonomy of Bug Tracking Process Smells: A Quantitative Analysis2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00026(138-147)Online publication date: Sep-2021
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Goyal ASardana N(2020)Analytical Study on Bug Triaging PracticesCognitive Analytics10.4018/978-1-7998-2460-2.ch088(1698-1725)Online publication date: 2020
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Alsawalqah HHijazi NEshtay MFaris HRadaideh AAljarah IAlshamaileh Y(2020)Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented PatternsApplied Sciences10.3390/app1005174510:5(1745)Online publication date: 3-Mar-2020
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Sajedi‐Badashian AStroulia E(2020)Guidelines for evaluating bug‐assignment researchJournal of Software: Evolution and Process10.1002/smr.225032:9Online publication date: 3-Sep-2020
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