Predicting Prime Path Coverage Using Regression Analysis

Resumo

Test coverage criteria help the tester to analyze the quality of the test suite, especially in an evolving system where it can be used to guide the prioritization of regression tests as well as the testing effort of new code. However, coverage analysis of more powerful criteria such as path coverage is still a challenging task due to the lack of supporting tools. In this paper, we evaluate the opportunity of using machine learning algorithms to estimate the prime-path coverage of a test suite at method level. We use a knowledge discovery in database framework and a dataset built from real-world projects to devise a regression model for prime-path prediction. Experimental results demonstrate that a suitable predictive model can be defined with RMSE 0.118 on cross-validation and an average error rate of 26% in a cross-project scenario. We also show how the tester can use the proposed strategy in a practical cross-project scenario.