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Issue No.10 - Oct. (2013 vol.39)
pp: 1345-1357
Akito Monden , Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Ikoma, Japan
Takuma Hayashi , R&D Center, NTT West Corp., Osaka, Japan
Shoji Shinoda , R&D Center, NTT West Corp., Osaka, Japan
Kumiko Shirai , R&D Center, NTT West Corp., Osaka, Japan
Junichi Yoshida , R&D Center, NTT West Corp., Osaka, Japan
Mike Barker , Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Ikoma, Japan
Kenichi Matsumoto , Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Ikoma, Japan
Until now, various techniques for predicting fault-prone modules have been proposed and evaluated in terms of their prediction performance; however, their actual contribution to business objectives such as quality improvement and cost reduction has rarely been assessed. This paper proposes using a simulation model of software testing to assess the cost effectiveness of test effort allocation strategies based on fault prediction results. The simulation model estimates the number of discoverable faults with respect to the given test resources, the resource allocation strategy, a set of modules to be tested, and the fault prediction results. In a case study applying fault prediction of a small system to acceptance testing in the telecommunication industry, results from our simulation model showed that the best strategy was to let the test effort be proportional to "the number of expected faults in a module × log(module size)." By using this strategy with our best fault prediction model, the test effort could be reduced by 25 percent while still detecting as many faults as were normally discovered in testing, although the company required about 6 percent of the test effort for metrics collection, data cleansing, and modeling. The simulation results also indicate that the lower bound of acceptable prediction accuracy is around 0.78 in terms of an effort-aware measure, Norm(Popt). The results indicate that reduction of the test effort can be achieved by fault prediction only if the appropriate test strategy is employed with high enough fault prediction accuracy. Based on these preliminary results, we expect further research to assess their general validity with larger systems.
Testing, Predictive models, Measurement, Software, Resource management, Companies, Accuracy,simulation, Complexity measures, fault prediction, quality assurance, resource allocation
Akito Monden, Takuma Hayashi, Shoji Shinoda, Kumiko Shirai, Junichi Yoshida, Mike Barker, Kenichi Matsumoto, "Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing", IEEE Transactions on Software Engineering, vol.39, no. 10, pp. 1345-1357, Oct. 2013, doi:10.1109/TSE.2013.21
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