Semantic-based and Learning-based Regression Test Selection focusing on Test Objectives

Machine learning-based regression test selection (MLRTS) has typically arisen in major cloud companies with full CI/CD pipelines. Various approaches for RTS are researched except for deep semantics of test cases, i. e. test objectives. Test objectives are essential but implicit information hard to calculate. In this paper, we propose a test architecture of a test objective-based MLRTS (TOMLRTS). First, word vectors are converted from explicitly written words in all regression test cases by Word2Vec. Second, semantic test objective clusters are formed by K-means++ to express all test objectives in the regression test cases. Third, distance vectors are constituted, whose elements are distances from each test case to the test objective clusters. Priorities of regression test cases are then calculated by MLRTS additionally according to the distance vectors. We additionally evaluate TOMLRTS compared to Facebook's MLRTS for commercial software. TOMLRTS selected test cases to detect bugs more rapidly.