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Measuring reliability growth of software by considering fault dependency, debugging time Lag functions and irregular fluctuation

Authors:

Abhishek TandonAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 35, Issue 3

Pages 1 - 11

https://doi.org/10.1145/1764810.1764831

Published: 11 May 2010 Publication History

Abstract

The progress of software testing is influenced by various uncertainty factors like effort expenditure, skill of test personal, testing tool, defect density, irregular state of open source project, and irregular state of software fault-report phenomena on the bug tracking system. Hence, there is an irregular fluctuation in fault detection/removal rate during testing phase. In software, the independence of failures can hardly be assumed and dependency of faults can also be considered as one of the factor for getting irregular fluctuation. In Literature, various software reliability growth models have been developed by considering fault dependency with various debugging time lag functions. But, none of the models have incorporated irregular fluctuation in their fault detection rate. Therefore, in this paper fault dependency based software reliability growth models have been developed by applying an It ∧o type Stochastic Differential Equations in order to incorporate (i) the irregular fluctuation in the fault detection process due to various uncertainty factor during testing phase and (ii) irregular state of software fault-report phenomena on the bug tracking system. The proposed stochastic differential equation based fault dependency models have been validated using (i) open source software fault count data where software fault-report phenomena on the bug tracking system keep an irregular state and (ii) a fault counting data with minor, major and critical faults. The proposed models have been compared with the existing fault dependency models. Various comparison criteria results for goodness of fit have also been presented in the paper.

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Singh, V.B., Kapur, P.K and Mashaallah Basirzadeh "Instructions Executed Dependent Software Reliability Growth Modeling for Open Source Software by Considering Change-point Published in proceeding of 4th National Conference on Computing for Nation Development-INDIACOM-2010" Bharatia Vidyapith, pp.399--404, New Delhi, 25-26 February, 2010.

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Cited By

Mishra GKapur PAggarwal A(2023)A generalized multi-upgradation SRGM considering uncertainty of random field operating environmentsInternational Journal of System Assurance Engineering and Management10.1007/s13198-023-01859-714:S1(328-336)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s13198-023-01859-7
Gusmanov KLeavens GGarcia APăsăreanu C(2018)On the adoption of neural networks in modeling software reliabilityProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3275433(962-964)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3275433
Lu CZhong JTakadama K(2018)An efficient ant colony system for coverage based test case prioritizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3205680(91-92)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3205680
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Index Terms

Measuring reliability growth of software by considering fault dependency, debugging time Lag functions and irregular fluctuation
1. General and reference
  1. Cross-computing tools and techniques
    1. Reliability
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 35, Issue 3

May 2010

151 pages

ISSN:0163-5948

DOI:10.1145/1764810

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Copyright © 2010 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 May 2010

Published in SIGSOFT Volume 35, Issue 3

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Cited By

Mishra GKapur PAggarwal A(2023)A generalized multi-upgradation SRGM considering uncertainty of random field operating environmentsInternational Journal of System Assurance Engineering and Management10.1007/s13198-023-01859-714:S1(328-336)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s13198-023-01859-7
Gusmanov KLeavens GGarcia APăsăreanu C(2018)On the adoption of neural networks in modeling software reliabilityProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3275433(962-964)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3275433
Lu CZhong JTakadama K(2018)An efficient ant colony system for coverage based test case prioritizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3205680(91-92)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3205680
Dadeau FGiorgetti ABouquet FEnderlin I(2018)Contract-based testing for PHP with PraspelJournal of Systems and Software10.1016/j.jss.2017.06.017136:C(209-222)Online publication date: 1-Feb-2018
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Öztürk M(2018)A bat-inspired algorithm for prioritizing test casesVietnam Journal of Computer Science10.1007/s40595-017-0100-x5:1(45-57)Online publication date: 1-Feb-2018
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Diwaker CTomar PPoonia RSingh V(2018)Prediction of Software Reliability using Bio Inspired Soft Computing TechniquesJournal of Medical Systems10.1007/s10916-018-0952-342:5(1-16)Online publication date: 1-May-2018
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Mann MTomar PSangwan O(2018)Bio-inspired metaheuristicsApplied Intelligence10.1007/s10489-017-1003-348:3(687-702)Online publication date: 1-Mar-2018
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Jiang SYi DJu XWang LLiu Y(2014)An approach for test data generation using program slicing and particle swarm optimizationNeural Computing and Applications10.1007/s00521-014-1692-z25:7-8(2047-2055)Online publication date: 1-Dec-2014
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Kumar PSingh Y(2013)Comparative analysis of software reliability predictions using statistical and machine learning methodsInternational Journal of Intelligent Systems Technologies and Applications10.1504/IJISTA.2013.05652912:3/4(230-253)Online publication date: 1-Sep-2013
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