research-article

Software test effort estimation

Authors:

Dharmender Singh Kushwaha,

A. K. MisraAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 33, Issue 3

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/1360602.1361211

Published: 01 May 2008 Publication History

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Abstract

Software Testing is an important process of software development that is performed to support and enhance reliability and quality of the software. It consist of estimating testing effort, selecting suitable test team, designing test cases, executing the software with those test cases and examining the results produced by those executions. Studies indicate that more than fifty percent of the cost of software development is devoted to testing, with the percentage for testing critical software being even higher. Unless we can predict the testing effort and find efficient ways to perform effective testing, the percentage of development costs devoted to testing will increase significantly coupled with mismatch in project costing and development schedule. In order to estimate the testing effort, this paper makes an attempt to establish Cognitive Information Complexity Measure (CICM) as an appropriate estimation tool.

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 33, Issue 3

May 2008

85 pages

ISSN:0163-5948

DOI:10.1145/1360602

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 2008

Published in SIGSOFT Volume 33, Issue 3

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Cited By

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Esposito MSarbazvatan STse TSilva-Atencio G(2024)The use of artificial intelligence for automatic analysis and reporting of software defectsFrontiers in Artificial Intelligence10.3389/frai.2024.14439567Online publication date: 11-Dec-2024
https://doi.org/10.3389/frai.2024.1443956
Venkatraman KAkashvarma MSiddharth S(2024)Enhancing Software Test Effort Estimation using Ensemble Learning Algorithms2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10626442(1-5)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10626442
Olufemi Ogunleye J(2023)Predictive Data Analysis Using Linear Regression and Random ForestData Integrity and Data Governance10.5772/intechopen.107818Online publication date: 26-Apr-2023
https://doi.org/10.5772/intechopen.107818
Sánchez-García ALópez-Martín CAbran A(2023)Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software ProjectsIEEE Access10.1109/ACCESS.2023.333780911(135235-135254)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3337809
Ogunleye J(2023)Finding Suitable Data Mining Techniques for Software Development Effort EstimationIntelligent Computing10.1007/978-3-031-37963-5_35(490-506)Online publication date: 20-Aug-2023
https://doi.org/10.1007/978-3-031-37963-5_35
Kaur AKaur K(2022)Systematic literature review of mobile application development and testing effort estimationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2018.11.00234:2(1-15)Online publication date: Feb-2022
https://doi.org/10.1016/j.jksuci.2018.11.002
López-Martín C(2022)Machine learning techniques for software testing effort predictionSoftware Quality Journal10.1007/s11219-020-09545-830:1(65-100)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11219-020-09545-8
Bluemke IMalanowska A(2021)Software Testing Effort Estimation and Related ProblemsACM Computing Surveys10.1145/344269454:3(1-38)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3442694
Sachan RKushwaha D(2020)Anti-Predatory NIA Based Approach for Optimizing Basic COCOMO Model2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence47617.2020.9058033(710-715)Online publication date: Jan-2020
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Ogunleye J(2020)Review of Current Data Mining Techniques Used in the Software Effort EstimationSoftware Engineering Perspectives in Intelligent Systems10.1007/978-3-030-63322-6_32(393-408)Online publication date: 16-Dec-2020
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