research-article

Enhancing Defect Prediction with Static Defect Analysis

Authors:

Lu ZhangAuthors Info & Claims

Internetware '15: Proceedings of the 7th Asia-Pacific Symposium on Internetware

Pages 43 - 51

https://doi.org/10.1145/2875913.2875922

Published: 06 November 2015 Publication History

Abstract

In the software development process, how to develop better software at lower cost has been a major issue of concern. One way that helps is to find more defects as early as possible, on which defect prediction can provide effective guidance. The most popular defect prediction technique is to build defect prediction models based on machine learning. To improve the performance of defect prediction model, selecting appropriate features is critical. On the other hand, static analysis is usually used in defect detection. As static defect analyzers detects defects by matching some well-defined "defect patterns", its result is useful for locating defects. However, defect prediction and static defect analysis are supposed to be two parallel areas due to the differences in research motivation, solution and granularity.

In this paper, we present a possible approach to improve the performance of defect prediction with the help of static analysis techniques. Specifically, we present to extract features based on defect patterns from static defect analyzers to improve the performance of defect prediction models. Based on this approach, we implemented a defect prediction tool and set up experiments to measure the effect of the features.

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Cited By

Yu XCogo FMcIntosh SGodfrey M(2024)Studying the impact of risk assessment analytics on risk awareness and code review performanceEmpirical Software Engineering10.1007/s10664-024-10443-x29:2Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1007/s10664-024-10443-x
Querel LRigby P(2021)Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)10.1109/ICPC52881.2021.00051(433-443)Online publication date: May-2021
https://doi.org/10.1109/ICPC52881.2021.00051
Qiao LLi XUmer QGuo P(2020)Deep learning based software defect predictionNeurocomputing10.1016/j.neucom.2019.11.067385:C(100-110)Online publication date: 14-Apr-2020
https://dl.acm.org/doi/10.1016/j.neucom.2019.11.067
Show More Cited By

Index Terms

Enhancing Defect Prediction with Static Defect Analysis
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Program analysis
    2. Program semantics

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cover image ACM Other conferences

Internetware '15: Proceedings of the 7th Asia-Pacific Symposium on Internetware

November 2015

247 pages

ISBN:9781450336413

DOI:10.1145/2875913

Copyright © 2015 ACM.

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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Key Laboratory of High Confidence Software Technologies: Key Laboratory of High Confidence Software Technologies, Ministry of Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2015

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the High-Tech Research and Development Program of China
the National Natural Science Foundation of China

Conference

Internetware '15

Internetware '15: The Seventh Asia-Pacific Symposium on Internetware

November 6, 2015

Wuhan, China

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Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

Yu XCogo FMcIntosh SGodfrey M(2024)Studying the impact of risk assessment analytics on risk awareness and code review performanceEmpirical Software Engineering10.1007/s10664-024-10443-x29:2Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1007/s10664-024-10443-x
Querel LRigby P(2021)Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)10.1109/ICPC52881.2021.00051(433-443)Online publication date: May-2021
https://doi.org/10.1109/ICPC52881.2021.00051
Qiao LLi XUmer QGuo P(2020)Deep learning based software defect predictionNeurocomputing10.1016/j.neucom.2019.11.067385:C(100-110)Online publication date: 14-Apr-2020
https://dl.acm.org/doi/10.1016/j.neucom.2019.11.067
Querel LRigby PLeavens GGarcia APăsăreanu C(2018)WarningsGuru: integrating statistical bug models with static analysis to provide timely and specific bug warningsProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3264599(892-895)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3264599
Pashchenko IDashevskyi SMassacci FBener ATurhan BBiffl S(2017)Delta-benchProceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1109/ESEM.2017.24(163-168)Online publication date: 9-Nov-2017
https://dl.acm.org/doi/10.1109/ESEM.2017.24

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