Test case prioritization (TCP) attempts to improve fault detection effectiveness by scheduling the important test cases to be executed earlier, where the importance is determined by some criteria or strategies. Adaptive random sequences (ARSs) can be used to improve the effectiveness of TCP based on white-box information (such as code coverage information) or black-box information (such as test input information). To improve the testing effectiveness for object-oriented software in regression testing, in this paper, we present an ARS approach based on clustering techniques using black-box information. We use two clustering methods:(1) clustering test cases according to the number of objects and methods, using the K-means and K-medoids clustering algorithms; and (2) clustered based on an object and method invocation sequence similarity metric using the K-medoids clustering algorithm. Our approach can construct ARSs that attempt to make their neighboring test cases as diverse as possible. Experimental studies were also conducted to verify the proposed approach, with the results showing both enhanced probability of earlier fault detection, and higher effectiveness than random prioritization and method coverage TCP technique.

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.

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Combinatorial interaction testing is a well-recognized testing method, and has been widely applied in practice, often with the assumption that all test cases in a combinatorial test suite have the same fault detection capability. However, when testing resources are limited, an alternative assumption may be that some test cases are more likely to reveal failure, thus making the order of executing the test cases critical. To improve testing cost-e®ectiveness, prioritization of combinatorial test cases is employed. The most popular approach is based on interaction coverage, which prioritizes combinatorial test cases by repeatedly choosing an unexecuted test case that covers the largest number of uncovered parameter value combinations of a given strength (level of interaction among parameters). However, this approach su®ers from some drawbacks. Based on previous observations that the majority of faults in practical systems can usually be triggered with parameter interactions of small strengths, we propose a new strategy of prioritizing combinatorial test cases by incrementally adjusting the strength values. Experimental results show that our method performs better than the random prioritization technique and the technique of prioritizing combinatorial test suites according to test case . Downloaded from www.worldscientific.com by UNIVERSITY OF OTAGO on 01/05/15. For personal use only.generation order, and has better performance than the interaction-coverage-based test prioritization technique in most cases.

a b s t r a c tContext: Adaptive random testing (ART), originally proposed as an enhancement of random testing, is often criticized for the high computation overhead of many ART algorithms. Mirror ART (MART) is a novel approach that can be generally applied to improve the efficiency of various ART algorithms based on the combination of ''divide-and-conquer'' and ''heuristic'' strategies. Objective: The computation overhead of the existing MART methods is actually on the same order of magnitude as that of the original ART algorithms. In this paper, we aim to further decrease the order of computation overhead for MART. Method: We conjecture that the mirroring scheme in MART should be dynamic instead of static to deliver a higher efficiency. We thus propose a new approach, namely dynamic mirror ART (DMART), which incrementally partitions the input domain and adopts new mirror functions. Results: Our simulations demonstrate that the new DMART approach delivers comparable failure-detection effectiveness as the original MART and ART algorithms while having much lower computation overhead. The experimental studies further show that the new approach also delivers a better and more reliable performance on programs with failure-unrelated parameters. Conclusion: In general, DMART is much more cost-effective than MART. Since its mirroring scheme is independent of concrete ART algorithms, DMART can be generally applied to improve the cost-effectiveness of various ART algorithms.

Abstract-Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

Combinatorial interaction testing is a widely used approach. In testing, it is often assumed that all combinatorial test cases have equal fault detection capability, however it has been shown that the execution order of an interaction test suite's test cases may be critical, especially when the testing resources are limited. To improve testing cost-effectiveness, test cases in the interaction test suite can be prioritized, and one of the best-known categories of prioritization approaches is based on "fixed-strength prioritization", which prioritizes an interaction test suite by choosing new test cases which have the highest uncovered interaction coverage at a fixed strength (level of interaction among parameters). A drawback of these approaches, however, is that, when selecting each test case, they only consider a fixed strength, not multiple strengths. To overcome this, we propose a new "aggregate-strength prioritization", to combine interaction coverage at different strengths. Experimental results show that in most cases our method performs better than the test-case-generation, reverse test-case-generation, and random prioritization techniques. The method also usually outperforms "fixed-strength prioritization", while maintaining a similar time cost.

Combinatorial interaction testing (CIT), a black-box testing method, has been well studied in recent years. It aims at constructing an effective interaction test suites, so as to identify the faults that are caused by interactions among parameters. After interaction test suites are generated by CIT, the execution order of test cases in the test suite becomes critical due to limited testing resources. To determine test case order, the prioritization of interaction test suites has been employed. As we know, random prioritization (RP) of test cases has been considered as simple but ineffective. Existing research unveils that adaptive random prioritization (ARP) of test cases is an alternative and promising candidate that may replace RP. However, previous ARP techniques may not be used to prioritize interaction test suites due to the lack of source-code-related information in interaction test suite, such as statement coverage, function coverage, or branch coverage. In this paper, we not only propose the ARP strategy in order to prioritize interaction test suites by using interaction coverage information, without the source-code-related information, but also unify the RP strategy and traditional interaction-coverage based prioritization strategy (ICBP). Additionally, simulation studies indicate that the ARP strategy performs better than the RP strategy, test-case-generation prioritization, and reverse test-casegeneration prioritization, and can also be more time-saving than ICBP while greatly maintaining similar, or even better, effectiveness.