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- Holt, Nina E., et al.
(författare)
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Empirical evaluations on the cost-effectiveness of state-based testing: An industrial case study
- 2014
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849 .- 1873-6025. ; 56:8, s. 890-910
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Tidskriftsartikel (refereegranskat)abstract
- Context: Test models describe the expected behavior of the software under test and provide the basis for test case and oracle generation. When test models are expressed as UML state machines, this is typically referred to as state-based testing (SBT). Despite the importance of being systematic while testing, all testing activities are limited by resource constraints. Thus, reducing the cost of testing while ensuring sufficient fault detection is a common goal in software development. No rigorous industrial case studies of SBT have yet been published. Objective: In this paper, we evaluate the cost-effectiveness of SBT on actual control software by studying the combined influence of four testing aspects: coverage criterion, test oracle, test model and unspecified behavior (sneak paths). Method: An industrial case study was used to investigate the cost-effectiveness of SBT. To enable the evaluation of SBT techniques, a model-based testing tool was configured and used to automatically generate test suites. The test suites were evaluated using 26 real faults collected in a field study. Results: Results show that the more detailed and rigorous the test model and oracle, the higher the fault-detection ability of SBT. A less precise oracle achieved 67% fault detection, but the overall cost reduction of 13% was not enough to make the loss an acceptable trade-off. Removing details from the test model significantly reduced the cost by 85%. Interestingly, only a 24–37% reduction in fault detection was observed. Testing for sneak paths killed the remaining eleven mutants that could not be killed by the conformance test strategies. Conclusions: Each of the studied testing aspects influences cost-effectiveness and must be carefully considered in context when selecting strategies. Regardless of these choices, sneak-path testing is a neces- sary step in SBT since sneak paths are common while also undetectable by conformance testing.
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| 2. |
- Rogstad, Erik, et al.
(författare)
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Test case selection for black-box regression testing of database applications
- 2013
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849 .- 1873-6025. ; 55:10, s. 1781-1795
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Tidskriftsartikel (refereegranskat)abstract
- Context: This paper presents an approach for selecting regression test cases in the context of large-scale, database applications. We focus on a black-box (specification-based) approach, relying on classification tree models to model the input domain of the system under test (SUT), in order to obtain a more practical and scalable solution. We perform an industrial case study where the SUT is a large database application in Norway’s tax department. Objective: We investigate the use of similarity-based test case selection for supporting black box regression testing of database applications. We have developed a practical approach and tool (DART) for functional black-box regression testing of database applications. In order to make the regression test approach scalable for large database applications, we needed a test case selection strategy that reduces the test execution costs and analysis effort. We used classification tree models to partition the input domain of the SUT in order to then select test cases. Rather than selecting test cases at random from each partition, we incorporated a similarity-based test case selection, hypothesizing that it would yield a higher fault detection rate. Method: An experiment was conducted to determine which similarity-based selection algorithm was the most suitable in selecting test cases in large regression test suites, and whether similarity-based selection was a worthwhile and practical alternative to simpler solutions. Results: The results show that combining similarity measurement with partition-based test case selection, by using similarity-based test case selection within each partition, can provide improved fault detection rates over simpler solutions when specific conditions are met regarding the partitions. Conclusions: Under the conditions present in the experiment the improvements were marginal. However, a detailed analysis concludes that the similarity-based selection strategy should be applied when a large number of test cases are contained in each partition and there is significant variability within partitions. If these conditions are not present, incorporating similarity measures is not worthwhile, since the gain is negligible over a random selection within each partition.
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