A Pattern-based Approach to Detect and Improve Non-descriptive Test Names
This addresses a specific issue for software developers by improving test name quality, but it is incremental as it builds on existing methods for test analysis.
The paper tackles the problem of non-descriptive unit test names in software development by proposing a pattern-based approach to detect and improve them, achieving a 95% true-positive rate in discriminating descriptive and non-descriptive names in an evaluation on 34,352 JUnit tests.
Unit tests are an important artifact that supports the software development process in several ways. For example, when a test fails, its name can provide the first step towards understanding the purpose of the test. Unfortunately, unit tests often lack descriptive names. In this paper, we propose a new, pattern-based approach that can help developers improve the quality of test names of JUnit tests by making them more descriptive. It does this by detecting non-descriptive test names and in some cases, providing additional information about how the name can be improved. Our approach was assessed using an empirical evaluation on 34352 JUnit tests. The results of the evaluation show that the approach is feasible, accurate, and useful at discriminating descriptive and non-descriptive names with a 95% true-positive rate.