Open AccessA Machine Learning Based Approach for Software Test Case Selection
Author(s) -
Victor Cheruiyot,
Baidya Nath Saha
Publication year - 2021
Publication title -
aijr proceedings
Language(s) - English
Resource type - Conference proceedings
ISSN - 2582-3922
DOI - 10.21467/proceedings.115.25
Subject(s) - computer science , machine learning , regression testing , feature selection , artificial intelligence , software , selection (genetic algorithm) , categorical variable , test data , test (biology) , task (project management) , test case , software regression , data mining , software system , software quality , software development , software construction , software engineering , regression analysis , programming language , engineering , paleontology , systems engineering , biology
Testing is conducted after developing each software to detect the defects which are then removed. However, it is very difficult task to test a non-trivial software completely. Hence, it’s important to test the software with important test cases. In this research, we developed a machine learning based software test case selection strategy for regression testing. To develop the method, we first clean and preprocess the data. Then we convet the categorical data to its numerical value. The we implement a natural language processing to calculate bag of features for text feature such as testcase title. We evaluate different machine learning models for test case selection. Experimental results demonstrate that machine learning based models can aovid manual labour of the domain experts for test case selection.