Software testing consumes a significant portion of software effort. Program entities such as branch or definition-use pairs (DUPs) are used in diverse software development tasks. In this study, the authors present a novel evolution-based approach to generating test data for all definition-use coverage. First, the subset of DUPs, which can ensure the coverage adequacy, is computed by a reduction algorithm for the whole DUPs. Then they apply a genetic algorithm to generate test data for the subset of DUPs. Furthermore, the fitness of an individual depends on the matching degree between the traversed path and the definition-clear path of each target DUP. They also investigate the coverage and the size of test cases of test data generation by applying the authors' approach on 15 widely used subject programs. The experimental results show that their approach can reduce the size of test cases that generated without affecting the coverage rate.
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.
Combinatorial interaction testing is a well-studied testing strategy, and has been widely applied in practice. Combinatorial interaction test suite, such as fixed-strength and variable-strength interaction test suite, is widely used for combinatorial interaction testing. Due to constrained testing resources in some applications, for example in combinatorial interaction regression testing, prioritization of combinatorial interaction test suite has been proposed to improve the efficiency of testing. However, nearly all prioritization techniques may only support fixed-strength interaction test suite rather than variable-strength interaction test suite. In this paper, we propose two heuristic methods in order to prioritize variablestrength interaction test suite by taking advantage of its special characteristics. The experimental results show that our methods are more effective for variable-strength interaction test suite by comparing with the technique of prioritizing combinatorial interaction test suites according to test case generation order, the random test prioritization technique, and the fixed-strength interaction test suite prioritization technique. Besides, our methods have additional advantages compared with the prioritization techniques for fixed-strength interaction test suite.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.