Automating Test Case Identification in Java Open Source Projects on GitHub

TL;DR

This paper investigates how test cases are written in Java open source projects on GitHub, proposing an automated method that accurately detects test cases and analyzes testing practices across diverse projects.

Contribution

It introduces a static analysis algorithm that detects 97% of test cases in Java projects, providing insights into testing practices and test identification challenges.

Findings

Weak correlation (r=0.655) between 'test' word occurrences and test cases

Proposed algorithm detects 97% of test cases

15% of classes use main() for testing without frameworks

Abstract

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word "test" in different natural languages; (ii) whether the number of occurrences of the word "test" correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3M GitHub projects. We analyzed 20,340 test cases in 803 classes manually and 170k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word "test" and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97% of test cases; (iii) 15% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future.