Regression testing assures software correctness after changes but is resource-intensive. Test Case Prioritisation (TCP) mitigates this by ordering tests to maximise early fault detection. Diversity-based TCP prioritises dissimilar tests, assuming they exercise different system parts and uncover more faults. Traditional static diversity-based TCP approaches (i.e., methods that utilise the dissimilarity of tests), like the state-of-the-art FAST approach, rely on textual diversity from test source code, which is effective but inefficient due to its relative verbosity and redundancies affecting similarity calculations. This paper is the first to study bytecode as the basis of diversity in TCP, leveraging its compactness for improved efficiency and accuracy. An empirical study on seven Defects4J projects shows that bytecode diversity improves fault detection by 2.3–7.8% over text-based TCP. It is also 2–3 orders of magnitude faster in one TCP approach and 2.5–6 times faster in FAST-based TCP. Filtering specific bytecode instructions improves efficiency up to fourfold while maintaining effectiveness, making bytecode diversity a superior static approach.

ABSTRACT There has been a significant amount of interest regarding the use of DBTtsfull in software testing over the past two decades. Diversity‐based testing (DBT) technique uses similarity metrics to leverage the dissimilarity between software artefacts—such as requirements, abstract models, programme structures or inputs—in order to address a software testing problem. DBT techniques have been used to assist in finding solutions to several different types of problems including generating test cases, prioritizing them and reducing very large test suites. This paper is a systematic mapping study of DBT techniques that summarizes the key aspects and trends of 167 papers that report the use of 79 different similarity metrics with 22 different types of software artefacts, which have been used by researchers to tackle 11 different types of software testing problems. We further present an analysis of the recent trends in DBT techniques and review the different application domains to which the techniques have been applied, giving an overview of the tools developed by researchers in order to do so. Finally, the paper identifies some DBT challenges that are potential topics for future work, such as exploring other diversity artefacts and measuring diversity for complex data input.

Regression test suites can have a large number of test cases, especially automatically generated ones, and tend to grow in size, making it costly to run the entire test suite. Test suite reduction aims to eliminate some test cases to reduce the test suite size and therefore reduce the cost of running it. In this paper, string distances on the text of the test cases are used as measures of similarity for reduction. A practical benefit of using string distance is that there is no need to run the test cases: the test suite source code is the only requirement, making the approach fast. We reduce test suites generated from Randoop and EvoSuite; two well-known test generation tools of Java programs. We implemented a string-based similarity reduction and compared it against random reduction. In the experiments, mutation scores using reduced test suites based on maximising string dissimilarity of test cases were higher than those for random reduction in over 70% of the test suites generated. Also, the results showed that test suites generated by Randoop can be drastically reduced in one case by 99% using the string-based similarity reduction approach while maintaining the fault-finding capabilities of the original test suite. Finally, on average, the normalised compression distance was found to be the best similarity metric choice in terms of fault-detection.