article

A study of cyclic dependencies on defect profile of software components

Authors:

Tosin Daniel Oyetoyan,

Daniela S. Cruzes,

Reidar ConradiAuthors Info & Claims

Journal of Systems and Software, Volume 86, Issue 12

Pages 3162 - 3182

https://doi.org/10.1016/j.jss.2013.07.039

Published: 01 December 2013 Publication History

Abstract

Background: Empirical evidence shows that dependency cycles among software components are pervasive in real-life software systems, although such cycles are known to be detrimental to software quality attributes such as understandability, testability, reusability, build-ability and maintainability. Research goals: Can the use of extended object-oriented metrics make us better understand the relationships among cyclic related components and their defect-proneness? Approach: First, we extend such metrics to mine and classify software components into two groups - the cyclic and the non-cyclic ones. Next, we have performed an empirical study of six software applications. Using standard statistical tests on four different hypotheses, we have determined the significance of the defect profiles of both groups. Results: Our results show that most defects and defective components are concentrated in cyclic-dependent components, either directly or indirectly. Discussion and conclusion: These results have important implications for software maintenance and system testing. By identifying the most defect-prone set in a software system, it is possible to effectively allocate testing resources in a cost efficient manner. Based on these results, we demonstrate how additional structural properties could be collected to understand component's defect proneness and aid decision process in refactoring defect-prone cyclic related components.

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Cited By

Feng QLiu SJi HMa XLiang P(2024)An empirical study of untangling patterns of two-class dependency cyclesEmpirical Software Engineering10.1007/s10664-023-10438-029:2Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1007/s10664-023-10438-0
Madeyski LLewowski T(2023)Detecting code smells using industry-relevant dataInformation and Software Technology10.1016/j.infsof.2022.107112155:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.infsof.2022.107112
Yang YHarman MKrinke JIslam SBinkley DZhou YXu BLo DApel SKhurshid S(2016)An empirical study on dependence clusters for effort-aware fault-proneness predictionProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering10.1145/2970276.2970353(296-307)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2970276.2970353
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A study of cyclic dependencies on defect profile of software components
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cover image Journal of Systems and Software

Journal of Systems and Software Volume 86, Issue 12

December, 2013

243 pages

ISSN:0164-1212

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Copyright © Elsevier Inc. © 2013.

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Elsevier Science Inc.

United States

Publication History

Published: 01 December 2013

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Cited By

Feng QLiu SJi HMa XLiang P(2024)An empirical study of untangling patterns of two-class dependency cyclesEmpirical Software Engineering10.1007/s10664-023-10438-029:2Online publication date: 12-Mar-2024
Madeyski LLewowski T(2023)Detecting code smells using industry-relevant dataInformation and Software Technology10.1016/j.infsof.2022.107112155:COnline publication date: 1-Mar-2023
Yang YHarman MKrinke JIslam SBinkley DZhou YXu BLo DApel SKhurshid S(2016)An empirical study on dependence clusters for effort-aware fault-proneness predictionProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering10.1145/2970276.2970353(296-307)Online publication date: 25-Aug-2016
Erturk EAkcapinar Sezer E(2016)Iterative software fault prediction with a hybrid approachApplied Soft Computing10.1016/j.asoc.2016.08.02549:C(1020-1033)Online publication date: 1-Dec-2016
Lo DApel SKhurshid S(2016)Proceedings of the 31st IEEE/ACM International Conference on Automated Software EngineeringundefinedOnline publication date: 25-Aug-2016
Erturk ESezer E(2015)A comparison of some soft computing methods for software fault predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.10.02542:4(1872-1879)Online publication date: 1-Mar-2015

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