Improving Test Automation Using Genetic Algorithm
Main Article Content
Abstract
Software testing is an important step in the creation of software products. Automation is critical in the software industry because it enables software testing firms to increase their test efficiency. Researchers have worked on a variety of automated ways for producing test data to evaluate generated software with various disadvantages. This paper therefore, presented Genetic Algorithm (GA)-based test techniques to automate the development of structural-oriented test data. In this work, random test cases are first generated, then, mutates testing is applied to check it. If satisfied, then process stops. Genetic Algorithms are utilized, since they offered a technique of automatically generating test cases.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
. Maheshwari V, Prasanna M. Generation of test case using automation in software systems–a review. Indian Journal of Science and Technology. 2015 Dec 24;8(35):1-9.
. Pandey M, Rajasekhara Babu M, Manasa J, Avinash K. Mobile based automation and security systems. Indian Journal of Science and Technology. 2015 Jan; 8(S2):12–6
. Mateen A, Nazir M, Awan SA. Optimization of test case generation using genetic algorithm (GA). arXiv preprint arXiv:1612.08813. 2016 Dec 28.
. Shakya S, Smys S. Reliable automated software testing through hybrid optimization algorithm. Journal of Ubiquitous computing and communication technologies (UCCT). 2020 Aug 4;2(03):126-35.
. https://www.analyticsvidhya.com/blog/2017/07/introduction-to-genetic-algorithm/. Accessed online December 7, 2021
. https://www.section.io/engineering-education/the-basics-of-genetic-algorithms-in-ml/. Accessed online December 7, 2021
. Singh A, Bhatia R, Singhrova A. Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics. Procedia computer science. 2018 Jan 1;132:993-1001.
. Haghighatkhah A, Banijamali A, Pakanen OP, Oivo M, Kuvaja P. Automotive software engineering: A systematic mapping study. Journal of Systems and Software. 2017 Jun 1;128:25-55.
. Elmishali A, Stern R, Kalech M. An artificial intelligence paradigm for troubleshooting software bugs. Engineering Applications of Artificial Intelligence. 2018 Mar 1;69:147-56.
. Miholca DL, Czibula G, Czibula IG. A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks. Information Sciences. 2018 May 1;441:152-70.
. Huang F, Bin LI. Software defect prevention based on human error theories. Chinese Journal of Aeronautics. 2017 Jun 1;30(3):1054-70.
. Jakubovski Filho HL, Ferreira TN, Vergilio SR. Preference based multi-objective algorithms applied to the variability testing of software product lines. Journal of Systems and Software. 2019 May 1;151:194-209.
. Li HW, Ren Y, Wang LN. Research on software testing technology based on fault tree analysis. Procedia Computer Science. 2019 Jan 1;154:754-8.
. Wang J, Zhang C. Software reliability prediction using a deep learning model based on the RNN encoder–decoder. Reliability Engineering & System Safety. 2018 Feb 1;170:73-82.
. Kaliraj S, Bharathi A. Path testing based reliability analysis framework of component based software system. Measurement. 2019 Oct 1;144:20-32.
. Juneja K. A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation. Applied Soft Computing. 2019 Apr 1;77:696-713.
. Xiao P, Liu B, Wang S. Feedback-based integrated prediction: Defect prediction based on feedback from software testing process. Journal of Systems and Software. 2018 Sep 1;143:159-71.
. Rhmann W, Pandey B, Ansari G, Pandey DK. Software fault prediction based on change metrics using hybrid algorithms: An empirical study. Journal of King Saud University-Computer and Information Sciences. 2020 May 1;32(4):419-24.
. Zhang XY, Zheng Z, Cai KY. Exploring the usefulness of unlabelled test cases in software fault localization. Journal of Systems and Software. 2018 Feb 1;136:278-90.
. Shao Y, Liu B, Wang S, Li G. Software defect prediction based on correlation weighted class association rule mining. Knowledge-Based Systems. 2020 May 21;196:105742.
. Sharma A, Patani R, Aggarwal A. Software testing using genetic algorithms. International Journal of Computer Science & Engineering Survey. 2016 Apr;7(2):21-33.