Evaluating the Trade-offs of Text-based Diversity in Test Prioritisation

Diversity-based techniques (DBT) have been cost-effective by prioritizing the most dissimilar test cases to detect faults at earlier stages of test execution. Diversity is measured on test specifications to convey how different test cases are from one another. However, there is little research on the trade-off of diversity measures based on different types of text-based specification (lexicographical or semantics). Particularly because the text content in test scripts vary widely from unit (e.g., code) to system-level (e.g., natural language). This paper compares and evaluates the cost-effectiveness in coverage and failures of different text-based diversity measures for different levels of tests. We perform an experiment on the test suites of 7 open source projects on the unit level, and 2 industry projects on the integration and system level. Our results show that test suites prioritised using semantic-based diversity measures causes a small improvement in requirements coverage, as opposed to lexical diversity that showed less coverage than random for system-level artefacts. In contrast, using lexical-based measures such as Jaccard or Levenshtein to prioritise code artefacts yield better failure coverage across all levels of tests. We summarise our findings into a list of recommendations for using semantic or lexical diversity on different levels of testing.