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Are automated static analysis tools worth it? An investigation into relative warning density and external software quality [article]

Alexander Trautsch and Steffen Herbold and Jens Grabowski
2021 arXiv   pre-print
Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code.  ...  When compared with all other changes, we find a statistically significant difference in one metric for all rules and two metrics for a subset of rules.  ...  This is also direct evidence for a common best practice in the use of static analysis tools: Appropriate rules for ASATs should be chosen for the project.  ... 
arXiv:2111.09188v2 fatcat:eywtozjo6nfkleiqfhadcvyzwm

Comparing static bug finders and statistical prediction

Foyzur Rahman, Sameer Khatri, Earl T. Barr, Premkumar Devanbu
2014 Proceedings of the 36th International Conference on Software Engineering - ICSE 2014  
Static analysis seeks to find defects using algorithms that process well-defined semantic abstractions of code.  ...  The all-important goal of delivering better software at lower cost has led to a vital, enduring quest for ways to find and remove defects efficiently and accurately.  ...  In a sense, one can view static analysis warnings as assessing a novel kind of property of source code, perhaps one more strongly allied with defects.  ... 
doi:10.1145/2568225.2568269 dblp:conf/icse/RahmanKBD14 fatcat:npu7xfc6nrbhdavgnfnfbmwhne

Fine-grained just-in-time defect prediction

Luca Pascarella, Fabio Palomba, Alberto Bacchelli
2019 Journal of Systems and Software  
In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in  ...  Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews  ...  Bacchelli and Palomba gratefully acknowledge the support of the Swiss National Science Foundation through the SNF Project No. PP00P2 170529.  ... 
doi:10.1016/j.jss.2018.12.001 fatcat:bajzy7c2nvhwjpajqpcpa4ux4u

Predicting accurate and actionable static analysis warnings

Joseph R. Ruthruff, John Penix, J. David Morgenthaler, Sebastian Elbaum, Gregg Rothermel
2008 Proceedings of the 13th international conference on Software engineering - ICSE '08  
Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative  ...  Static analysis tools report software defects that may or may not be detected by other verification methods.  ...  YuQian Zhou, Simon Quellen Field, and Larry Zhou contributed to the static analysis infrastructure at Google.  ... 
doi:10.1145/1368088.1368135 dblp:conf/icse/RuthruffPMER08 fatcat:rn3vpnuawfcyljoutykvabzgsq

On the differences between quality increasing and other changes in open source Java projects [article]

Alexander Trautsch, Johannes Erbel, Steffen Herbold, Jens Grabowski
2021 arXiv   pre-print
Static analysis tools also include boundary values for complexity and size that generate warnings for developers.  ...  Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality.  ...  Acknowledgements We want to thank the GWDG Göttingen 14 for providing us with computing resources within their HPC-Cluster.  ... 
arXiv:2109.03544v3 fatcat:xx4oslzg4zcnnlllb2fngboygm

Clustering static analysis defect reports to reduce maintenance costs

Zachary P. Fry, Westley
2013 2013 20th Working Conference on Reverse Engineering (WCRE)  
Static analysis tools facilitate software maintenance by automatically identifying bugs in source code. However, for large systems, these tools often produce an overwhelming number of defect reports.  ...  We evaluate our technique using 8,948 defect reports produced by the Coverity Static Analysis and FindBugs tools in both C and Java programs totaling over 14 million lines of code.  ...  ACKNOWLEDGMENTS The authors are sincerely indebted to Andy Chou of Coverity for initial ideas, guidance, and technical support.  ... 
doi:10.1109/wcre.2013.6671303 dblp:conf/wcre/FryW13 fatcat:rzccesx5kbcexmrk7eiqsnyiyy

Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy) [article]

Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu, Tim Menzies
2021 arXiv   pre-print
In this paper, we look for actionable warnings within a sample of 5,675 actionable warnings seen in 31,058 static code warnings from FindBugs.  ...  Static code warning tools often generate warnings that programmers ignore.  ...  Background Studying Static Code Warnings Static code warning tools detect potential static code defects in source code or executable files at the stage of software product development.  ... 
arXiv:2006.00444v3 fatcat:pldgzqy26zcjrcvroadpa2pz44

Predicting Defective Lines Using a Model-Agnostic Technique

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, Kenichi Matsumoto
2020 IEEE Transactions on Software Engineering  
Defect prediction models are proposed to help a team prioritize source code areas files that need Software Quality Assurance (SQA) based on the likelihood of having defects.  ...  Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective identification time.  ...  Static Analysis Static analysis is a tool that checks source code and reports warnings (i.e., common errors such as null pointer dereferencing and buffer overflows) at the line level.  ... 
doi:10.1109/tse.2020.3023177 fatcat:vnkikgjuxbea7c2xahtmyrkogy

Predicting Defective Lines Using a Model-Agnostic Technique [article]

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, Kenichi Matsumoto
2020 arXiv   pre-print
Defect prediction models are proposed to help a team prioritize source code areas files that need Software QualityAssurance (SQA) based on the likelihood of having defects.  ...  Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective line identification time.  ...  Static Analysis Static analysis is a tool that checks source code and reports warnings (i.e., common errors such as null pointer dereferencing and buffer overflows) at the line level.  ... 
arXiv:2009.03612v1 fatcat:ggqapwzdwzhoxlc3es7xbopuwe

Problems with SZZ and Features: An empirical study of the state of practice of defect prediction data collection [article]

Steffen Herbold, Alexander Trautsch, Fabian Trautsch, Benjamin Ledel
2021 arXiv   pre-print
Most defect prediction data sets provide only static code metrics as features, while research indicates that other features are also important.  ...  Context: The SZZ algorithm is the de facto standard for labeling bug fixing commits and finding inducing changes for defect prediction data.  ...  We also want to thank the GWDG for the support in using their high performance computing infrastructure, that enabled the collection of the large amounts of software metric data.  ... 
arXiv:1911.08938v3 fatcat:xrj2fi7o6jbdfbym2jdflgeex4

The Relation of Test-Related Factors to Software Quality: A Case Study on Apache Systems

Fabiano Pecorelli, Fabio Palomba, Andrea De Lucia
2021 Empirical Software Engineering  
The key findings of the study show that, when controlling for other metrics (e.g., size of the production class), test-related factors have a limited connection to post-release defects.  ...  In this paper, we propose a comprehensive case study on how test-related factors relate to production code quality in Apache systems.  ...  p <0.1 Acknowledgments The authors would like to sincerely thank the Associate Editor and anonymous Reviewers for the insightful comments and feedback provided during the review process.  ... 
doi:10.1007/s10664-020-09891-y fatcat:psfducgepfatjovzinfjchifpa

A Bayesian Network Based Approach for Change Coupling Prediction

Yu Zhou, Michael Würsch, Emanuel Giger, Harald C. Gall, Jian Lü
2008 2008 15th Working Conference on Reverse Engineering  
Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available.  ...  Abstract Source code coupling and change history are two important data sources for change coupling analysis.  ...  into FAMIX model [4] , and extracting the fine grained source change information [10] .  ... 
doi:10.1109/wcre.2008.39 dblp:conf/wcre/ZhouWGGL08 fatcat:c3kgvsbmlrbspll6fmvvsvysia

A survey and taxonomy of approaches for mining software repositories in the context of software evolution

Huzefa Kagdi, Michael L. Collard, Jonathan I. Maletic
2007 Journal of Software Maintenance and Evolution Research and Practice  
A comprehensive literature survey on approaches for mining software repositories (MSR) in the context of software evolution is presented.  ...  A taxonomy is derived from the analysis of this literature and presents the work via four dimensions: the type of software repositories mined (what), the purpose (why), the adopted/invented methodology  ...  The results obtained from ACKNOWLEDGEMENTS We thank the reviewers for their detailed comments and suggestions. These were instrumental in helping us improve the presentation of the survey.  ... 
doi:10.1002/smr.344 fatcat:zri7uxf7aba6forbe77gkmw2ru

Predicting defects in SAP Java code: An experience report

Tilman Holschuh, Markus Pauser, Kim Herzig, Thomas Zimmermann, Rahul Premraj, Andreas Zeller
2009 2009 31st International Conference on Software Engineering - Companion Volume  
In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component  ...  Predicting Defects in a Nutshell. We map previous defects to components and relate the resulting quality to component features. These features can then be used to predict future quality.  ...  We thank Günther Limböck of SAP Quality Governance & Production (QGP) for enabling this study and his ongoing support. Jürgen Heymann (QGP) provided lots of valuable insights during the study.  ... 
doi:10.1109/icse-companion.2009.5070975 dblp:conf/icse/HolschuhPHZPZ09 fatcat:joemoen5xzgcxgdpi527ardysq

Perceptions, Expectations, and Challenges in Defect Prediction

Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, Xiaohu Yang
2018 IEEE Transactions on Software Engineering  
Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear.  ...  To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction  ...  ACKNOWLEDGMENTS The authors would like to thank all survey participants for responding our survey.  ... 
doi:10.1109/tse.2018.2877678 fatcat:da5ehjqssjbjpn2jkktfi334za
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