research-article

Assessment of software testing time using soft computing techniques

Authors:

Yogesh SinghAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 37, Issue 1

Pages 1 - 6

https://doi.org/10.1145/2088883.2088895

Published: 27 January 2012 Publication History

Abstract

Application of a soft computing approach in place of traditional statistical techniques has shown a remarkable improvement in reliability prediction. This paper examines and compares Linear Regression (LR) and five machine learning methods: (Artificial Neural Network, Support Vector Machine, Decision Tree, Fuzzy Inference System and Adaptive Neuro-Fuzzy Inference System). These methods are explored empirically to find the effect of severity of errors for the assessment of software testing time. We use two publicly available failure datasets to analyse and compare the regression and machine learning methods for assessing the software testing time. The performance of the proposed model is compared by computing mean absolute error (MAE) and root mean square error (RMSE). Based on the results from rigours experiments, it is observed that model accuracy using FIS and ANFIS method is better and outperformed the model predicted using linear regression and other machine learning methods. Finally, we conclude that Adaptive Neuro-fuzzy Inference System is useful in constructing software quality models having better capability of generalization and less dependent on sample size.

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Cited By

Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111934
Sharma DChandra P(2017)Soft Computing Based Software Testing – A Concise TravelogueProceedings of Sixth International Conference on Soft Computing for Problem Solving10.1007/978-981-10-3325-4_22(220-228)Online publication date: 13-Apr-2017
https://doi.org/10.1007/978-981-10-3325-4_22
Sharma DChandra P(2017)Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief ReviewProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_53(535-546)Online publication date: 19-Aug-2017
https://doi.org/10.1007/978-3-319-60618-7_53
Show More Cited By

Index Terms

Assessment of software testing time using soft computing techniques
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 37, Issue 1

January 2012

115 pages

ISSN:0163-5948

DOI:10.1145/2088883

Issue’s Table of Contents

Copyright © 2012 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2012

Published in SIGSOFT Volume 37, Issue 1

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Cited By

Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111934
Sharma DChandra P(2017)Soft Computing Based Software Testing – A Concise TravelogueProceedings of Sixth International Conference on Soft Computing for Problem Solving10.1007/978-981-10-3325-4_22(220-228)Online publication date: 13-Apr-2017
https://doi.org/10.1007/978-981-10-3325-4_22
Sharma DChandra P(2017)Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief ReviewProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_53(535-546)Online publication date: 19-Aug-2017
https://doi.org/10.1007/978-3-319-60618-7_53
Li YNg S(2016)Fuzzy logic on reading recommendation system2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP)10.1109/INFRKM.2016.7806337(67-70)Online publication date: Aug-2016
https://doi.org/10.1109/INFRKM.2016.7806337

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