Topic Modeling Based Warning Prioritization from Change Sets of Software Repository

Jung Been Lee, Taek Lee, Hoh Peter In

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1461-1479
Number of pages19
JournalJournal of Computer Science and Technology
Volume35
Issue number6
DOIs
Publication statusPublished - 2020 Nov

Keywords

  • automated static analysis
  • topic modeling
  • warning prioritization

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics

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