Prioritizing unit testing effort using software metrics and machine learning classifiers

Abstract

Unit testing plays a crucial role in object-oriented software quality assurance. Unfortunately, software testing is often conducted under severe pressure due to limited resources and tight time constraints. Therefore, testing efforts have to be focused, particularly on critical classes. As a consequence, testers do not usually cover all software classes. Prioritizing unit testing effort is a crucial task. We previously investigated a unit testing prioritization approach based on software information histories. We analyzed different attributes of ten open-source Java software systems tested using the JUnit framework. We used machine learning classifiers (Multivariate Logistic Regression and Naïve Bayes) to obtain, for each system, a set of classes to be tested. The obtained sets of candidate classes have been compared to the sets of classes for which JUnit test cases have been actually developed by testers. The cross system validation (CSV) technique results showed, among others, that the sets of candidate classes suggested by machine learning classifiers properly reflect the testers' selection. In this paper, we extend our previous work by investigating more classifiers and using leave one system out validation (LOSOV) technique. This LOSOV technique uses a combination of training datasets from different systems. The obtained results indicate that: (1) the new classifiers correctly suggest classes to be tested, and (2) tested classes are particularly well predicted in the case of large-size systems.