Modeling software testing costs and risks using fuzzy logic paradigm

The overall lifecycle cost associated with product failures exceeds 10% of yearly corporations' turnover. A major factor contributing to this loss is ineffective performance of software and systems Verification, Validation and Testing (VVT). Given these realities, we proposed a set of quantitative probabilistic models for estimating costs and risks stemming from carrying out any given VVT strategy [Engel, A., Barad, M., 2003. A methodology for modeling VVT risks and costs. Systems Engineering Journal 6 (3), 135-151, Wiley InterScience, Online ISSN: 1520-6858, Print ISSN: 1098-1241]. We also demonstrated that quality costs in software-intensive projects are likely to consume as much as 60% of the development budget. Finally, we showed that project cost and duration could be reduced by optimizing the VVT strategy, yielding about 10-15% reduction in development costs and project schedule [Engel, A., Shachar, S., 2006. Measuring and optimizing systems' quality costs and project duration. Systems Engineering Journal 9 (3), 259-280]. A key problem associated with such cost and time estimates is that input data are imprecise by nature. Certain parameters are better captured using tuple structures (e.g. Minimum, Most-likely and Maximum values). Other parameters can be better encapsulated using linguistic terms such as "High" or "Low". This paper extends the above research by modeling the problem using the fuzzy logic paradigm. We estimate the quality cost occurring during the development of software for an avionic suite in a fighter aircraft and demonstrate that applying fuzzy logic methodology yields results comparable to estimations based on models using the probabilistic paradigm (less than 4% differences in each of the five VVT cost categories).