An experimental assessment of module documentation-based testing

Baharom, Salmi; Shukur, Zarina

doi:10.1016/j.infsof.2011.01.005

Cited by 8 publications

(6 citation statements)

References 56 publications

(62 reference statements)

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“…Therefore, it is impossible to explore the space of entire states with limited time resources and memories. SSP is a test case generation technique for modules [2][3][4]. The states are partitioned based on state's sensitivity towards events, conditions (pre-conditions) and actions (post-conditions).…”

Section: State-sensitivity Partitioning (Ssp)mentioning

confidence: 99%

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Genetic Algorithm Application for Enhancing State-Sensitivity Partitioning

Sultan

Baharom

Ghani

et al. 2015

Testing Software and Systems

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Software testing is the most crucial phase in software development life cycle which intends to find faults as much as possible. Test case generation leads the research in software testing. So, many techniques were proposed for the sake of automating the test case generation process. State sensitivity partitioning is a technique that partitions the entire states of a module. The generated test cases are composed of sequences of events. However, there is an infinite set of sequences with no upper bound on the length of a sequence. Thus, a lengthy test sequence might be encountered with redundant data states, which will increase the size of test suite and, consequently, the process of testing will be ineffective. Therefore, there is a need to optimize those test cases generated by SSP. GA has been identified as the most common potential technique among several optimization techniques. Thus, GA is investigated to integrate it with the existing SSP. This paper addresses the issue on deriving the fitness function for optimizing the sequence of events produced by SSP.

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Section: State-sensitivity Partitioning (Ssp)mentioning

confidence: 99%

“…Hence, a number of techniques were proposed for improving the effectiveness and efficiency of faults detection. State-sensitivity portioning (SSP) is one of them [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Application for Enhancing State-Sensitivity Partitioning

Sultan

Baharom

Ghani

et al. 2015

Testing Software and Systems

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“…Recently, it has experimentally been shown that mutation analysis can also be an accurate predictor of actual fault detection effectiveness of testing approaches [31]. On this regard, many researchers [38][39][40] have relied on applying mutation analysis as part of the experimental process to assess the effectiveness of different testing technqiues. Thus, we believe that the mutation analysis for measuring the fault detection effectiveness of the AOP testing approaches would be worthwhile and can provide experimental evidence in comparison with the conventional effectiveness analysis i.e.…”

Section: Context Selectionmentioning

confidence: 99%

Empirical evaluation of the fault detection effectiveness and test effort efficiency of the automated AOP testing approaches

Parizi

Ghani

Abdullah

et al. 2011

Information and Software Technology

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a b s t r a c tContext: Testing process is a time-consuming, expensive, and labor-intensive activity in any software setting including aspect-oriented programming (AOP). To reduce the testing costs, human effort, and to achieve the improvements in both quality and productivity of AOP, it is desirable to automate testing of aspect-oriented programs as much as possible. Objective: In recent past years, a lot of research effort has been devoted to testing aspect-oriented programs but less effort has been dedicated to the automated AOP testing. This denotes that the current research on automated AOP testing is not sufficient and is still in a stage of infancy. In order to advance the state of the research in this area and to provide testers of AOP-based projects with a comparison basis, a detailed evaluation of the current automated AOP testing approaches in a thorough and experimental manner is required. Thus, the objective of this paper is to provide such evaluation of the current approaches. Method: In this paper, we carry out an empirical study based on mutation analysis to examine four (namely Wrasp, Aspectra, Raspect, and EAT) existing automated AOP testing approaches, particularly their underlying test input generation and selection strategies, with regard to fault detection effectiveness. In addition, the approaches are compared in terms of required effort in detecting faults as part of efficiency evaluation. Results: The experimental results and comparison provided insights into the effectiveness and efficiency of automated AOP testing with their respective strengths and weaknesses. Results showed that EAT is more effective than the other automated AOP testing approaches but not significant for all approaches. EAT was found to be significantly better than Wrasp at 95% confidence level (i.e. p < 0.05), but not significantly better than Aspectra or Raspect. Concerning the test effort efficiency, Wrasp was significantly (p < 0.05) efficient with requiring the lowest amount of test effort compared to the other approaches. Whereas, EAT showed to be not very efficient by recording the highest amount of test effort. Conclusion: This implies that EAT can currently be the most effective automated AOP testing approach but perhaps less efficient. More generally, search-based testing (as underlying strategy of EAT approach) might achieve better effectiveness but at the cost of greater test effort compared to random testing (as underlying strategy of other approaches).

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“…Analysis the control flow functions logical relationship between the functions and get the function calling paths. It is different from the function relation of inclusion, the function contains relationship just analysis the function which contains, and does not consider the logical relationship [2] . FCP idea makes the basic path test workload exponentially reduced, making path oriented testpossible [3] .…”

Section: Introductionmentioning

confidence: 99%