Test case prioritization (TCP) has been widely used in software testing, which aims to execute test cases that are more likely to detect faults earlier than others. Among many proposed TCP approaches, lexicographical ordering-based TCP (LO-TCP) can effectively resolve ties encountered in the prioritization process, leading to better performance than original TCP approaches. However, the current LO-TCP needs to use the white-box information such as the code coverage of the program under test, which may be infeasible in some black-box testing applications such as software product lines (SPLs). In this article, we transfer the traditional LO-TCP to SPL testing by leveraging test configuration coverage instead of code coverage, and also empirically conduct some simulations and evaluate the large-scale real-world programs with real faults. The experimental results show that LO-TCP can have better performance for testing SPLs, as compared with traditional TCP approaches.

Abstract Test case prioritization (TCP) aims at scheduling test case execution so that more important test cases are executed as early as possible. Many TCP techniques have been proposed, according to different concepts and principles, with dissimilarity‐based TCP (DTCP) prioritizing tests based on the concept of test case dissimilarity: DTCP chooses the next test case from a set of candidates such that the chosen test case is farther away from previously selected test cases than the other candidates. DTCP techniques typically only use one aspect/granularity of the information or features from test cases to support the prioritization process. In this article, we adopt the concept of data fusion to propose a new family of DTCP techniques, data‐fusion‐driven DTCP (DDTCP), which attempts to use different information granularities for prioritizing test cases by dissimilarity. We performed an empirical study involving 30 versions of five subject programs, investigating the testing effectiveness and efficiency by comparing DDTCP against DTCP techniques that use a dissimilarity granularity. The experimental results show that not only does DDTCP have better fault‐detection rates than single‐granularity DTCP techniques, but it also appears to only incur similar prioritization costs. The results also show that DDTCP remains robust over multiple system releases.

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.