Article

Static analysis tools as early indicators of pre-release defect density

Authors:

Nachiappan Nagappan,

Thomas BallAuthors Info & Claims

ICSE '05: Proceedings of the 27th international conference on Software engineering

Pages 580 - 586

https://doi.org/10.1145/1062455.1062558

Published: 15 May 2005 Publication History

Abstract

During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.

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Published In

cover image ACM Conferences

ICSE '05: Proceedings of the 27th international conference on Software engineering

May 2005

754 pages

ISBN:1581139632

DOI:10.1145/1062455

General Chair:
Gruia-Catalin Roman
Washington University in St. Louis
,
Program Chairs:
William Griswold
University of California, San Diego
,
Bashar Nuseibeh
The Open University, UK

Copyright © 2005 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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Published: 15 May 2005

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ICSE05: 27th International Conference on Software Engineering

May 15 - 21, 2005

MO, St. Louis, USA

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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https://doi.org/10.32604/cmc.2024.057697
Cuadrado JÁvila DPérez-Soler SCañizares PGuerra Ede Lara J(2024)Integrating Static Quality Assurance in CI Chatbot Development WorkflowsIEEE Software10.1109/MS.2024.340155141:5(60-69)Online publication date: 1-Sep-2024
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Rajkó RSiket IHegedűs PFerenc R(2024)Development of Partial Least Squares Regression with Discriminant Analysis for Software Bug PredictionHeliyon10.1016/j.heliyon.2024.e35045(e35045)Online publication date: Jul-2024
https://doi.org/10.1016/j.heliyon.2024.e35045
Nikravesh NKeyvanpour M(2024)Parameter tuning for software fault prediction with different variants of differential evolutionExpert Systems with Applications10.1016/j.eswa.2023.121251237(121251)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121251
Guo ZTan TLiu SLiu XLai WYang YLi YChen LDong WZhou Y(2023)Mitigating False Positive Static Analysis Warnings: Progress, Challenges, and OpportunitiesIEEE Transactions on Software Engineering10.1109/TSE.2023.332966749:12(5154-5188)Online publication date: 1-Dec-2023
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Lenarduzzi VPecorelli FSaarimaki NLujan SPalomba F(2023)A critical comparison on six static analysis tools: Detection, agreement, and precisionJournal of Systems and Software10.1016/j.jss.2022.111575198(111575)Online publication date: Apr-2023
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