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Regular Paper

Topic Modeling Based Warning Prioritization from Change Sets of Software Repository

College of Informatics, Korea University, Seoul 02841, Korea
College of Knowledge-Based Services Engineering, Sungshin University, Seoul 02844, Korea
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Abstract

Many existing warning prioritization techniques seek to reorder the static analysis warnings such that true positives are provided first. However, excessive amount of time is required therein to investigate and fix prioritized warnings because some are not actually true positives or are irrelevant to the code context and topic. In this paper, we propose a warning prioritization technique that reflects various latent topics from bug-related code blocks. Our main aim is to build a prioritization model that comprises separate warning priorities depending on the topic of the change sets to identify the number of true positive warnings. For the performance evaluation of the proposed model, we employ a performance metric called warning detection rate, widely used in many warning prioritization studies, and compare the proposed model with other competitive techniques. Additionally, the effectiveness of our model is verified via the application of our technique to eight industrial projects of a real global company.

Keywords

topic modelingautomated static analysiswarning prioritization

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Journal of Computer Science and Technology
Volume 35 Issue 6,
November 2020
Pages 1461-1479
DOI: 10.1007/s11390-020-0047-8
Cite this article:
Lee J-B, Lee T, In HP. Topic Modeling Based Warning Prioritization from Change Sets of Software Repository. Journal of Computer Science and Technology, 2020, 35(6): 1461-1479. https://doi.org/10.1007/s11390-020-0047-8

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Received: 19 September 2019
Revised: 01 May 2020
Published: 30 November 2020
©Institute of Computing Technology, Chinese Academy of Sciences 2020
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