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Recently, software quality issues have come to be seen as an important subject as we see an enormous growth of agencies involved in software industries. However, these agencies cannot guarantee the quality of their products; thus leaving users in uncertainties. Software certification is the extension of quality by means that quality needs to be measured prior to certification granting process. However, building accurate certification model is hard due to the lack of data in the domain of software engineering. This research participates in solving the problem of evaluating software quality by proposing a model that uses a fuzzy inference engine to integrate both of the processes–driven and application-driven quality assurance strategies. The key idea of the suggested model is to improve the compactness and the interpretability of the model’s fuzzy rules via employing an ant colony optimization algorithm (ACO), which tries to find a good rule description by set of compound rules initially expressed with traditional single rules. The proposed model is an adaptive one that can be seen as taking certification models that have already been built from software quality domain data and adapting them to context-specific data. The model has been tested by a case study and the results have demonstrated feasibility and practicality of the model in a real environment.

Bio-Inspired Intelligent System for Software Quality Certification