Towards prioritize event sequence test cases using machine learning approach

• Main Authors: Ahmad, Johanna, Baharom, Salmi, Abd. Ghani, Abdul Azim, Zulzalil, Hazura, Din, Jamilah
• Format: Article
• Published: Institute of Advanced Scientific Research, Inc. 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/93606/; http://dx.doi.org/10.5373/JARDCS/V12SP7/20202269

Testing is one of the crucial phases in software development cycle. Without a proper plan, failure to deliver the product to the customer on time might happen. Furthermore, increasing cost, resources and time to test might also increase due to failure to plan the testing phase. Due to that reason, number of techniques has been proposed to increase the effectiveness of testing, and test case prioritization is one of it. In previous research, the researchers combined 6 factors to prioritize event sequence test cases. Realizing machine learning is one of the new approaches in software testing, the researchers apply naïve bayes approach into the pairwise events. The naïve bayes will calculate the probability for each of test case. The details of how the prioritization process after the implementation of naïve bayes will be explain in future research since this is ongoing research since 2015.