Hybrid swarm intelligence-based software testing techniques for improving quality of component based software
Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques usin...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-06-01.
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| Online Access: | Get fulltext |
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| LEADER | 02459 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24728_15112 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Palak, Palak |e author |
| 100 | 1 | 0 | |a MAHARSHI DAYANAND UNIVERSITY |e contributor |
| 700 | 1 | 0 | |a Gulia, Preeti |e author |
| 700 | 1 | 0 | |a Gill, Nasib Singh |e author |
| 245 | 0 | 0 | |a Hybrid swarm intelligence-based software testing techniques for improving quality of component based software |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24728 | ||
| 520 | |a Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a software testing; artificial bee colony; soft computing; | ||
| 690 | |a artificial bee colony; automation testing; metaheuristics; soft computing; test case selection; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 3: June 2021; 1716-1722 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24728/15112 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24728/15112 |z Get fulltext |