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International Journal of Engineering and Techniques(IJET) Paper Title : MACHINE LEARNING APPLIED TO SOFTWARE TESTING

ISSN : 2395-1303
Year of Publication : 2020
10.29126/23951303/IJET-V6I2P15
Authors:--Manikkannan D, R. Bhumi Devi, V.V. Nithyaa Shri, B. Bala Barathy

         



MLA Style: Manikkannan D, R. Bhumi Devi, V.V. Nithyaa Shri, B. Bala Barathy MACHINE LEARNING APPLIED TO SOFTWARE TESTING " Volume 6 - Issue 2(1-7) March - April,2020 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org

APA Style: Manikkannan D, R. Bhumi Devi, V.V. Nithyaa Shri, B. Bala Barathy MACHINE LEARNING APPLIED TO SOFTWARE TESTING " Volume 6 - Issue 2(1-7) March - April,2020 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org

Abstract
Software testing is an integral part of developing software which aims to identify the errors and faults. The budget involved in developing the software exceeds if the fault is not detected and corrected at the right time. Also, testing is error prone. In order to overcome such problems, an attempt is made to automate software testing by applying machine learning algorithms which will minimize the error and the cost involved in software testing. Software testing activities include test case generation and oracle testing among others. Linear regression, decision tree and random forest algorithms are the machine learning algorithms that are used.

Reference
[1] Vinicius H. S. Durelli, Rafael S. Durelli, Simone S. Borges, Andre T. Endo, Marcelo M. Eler, Diego R. C. Dias, Marcelo P.Guimar Aes, “Machine Learning Applied To Software Testing: A Systematic Mapping Study”, IEEE Transactions on Reliability, vol. 68, issue 3, pp. 1189- 1212, 2019, doi: 10.1109/TR.2019.2892517 [2] Dionny Santiago, Tariq M. King, Peter J. Clarke, “AI- Driven Test Generation: Machines Learning From Human Testers”, 2018, doi: 10.25148/etd.fidc007028 [3] Rajesh Kumar Sahoo, Deeptimanta Ojha, durga Prasad Mohapatra, Manas Ranjan Patra, “Automated Test Case Generation And Optimization: A Comparative Review”, International Journal of Computer Science & Information Technology (IJCSIT), vol. 8, no. 5, Oct. 2016, doi: 10.5121/ijcsit.2016.8502 [4] Pranali Mahadik, Debnath Bhattacharyya, Hye-jin Kim, “Techniques For Automated Test Cases Generation: A Review”, International Journal of Software Engineering and its Applications 10(12):13-20, Dec. 2016, doi: 10.14257/ijseia.2016.10.12.02

Keywords
software testing, machine learning, linear regression, decision tree, random forest

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