Recently, software quality issues have emerged to be recognized as a fundamental point as we actualize an extensive growth of organizations involved in software industries. Still, these organizations cannot ensure the quality of their products; therefore abandoning customers in uncertainties. Software certification is the branch of quality by means that quality requires to be measured prior to certification admitting process. However, creating an official certification model is difficult due to the deficiency of data in the domain of software engineering. This research participates in solving the problem of assessing software quality by introducing a model that handles a fuzzy inference engine to mix both of the processes-driven and application-driven quality assurance procedures. The fundamental purpose of the suggested model is to enhance the compactness and the interpretability of the system's fuzzy rules via engaging an ant colony optimization algorithm (ACO), which attempts to discover a good rule description by a set of compound rules initially represented with traditional single rules. The proposed model is a fitting one that can be seen as practicing certification models that have already been created from software quality domain data and modifying them to a context-specific data. The model has been tested by a case study and the results have confirmed feasibility and practicality of the model in a real environment. (C) 2016 Published by Elsevier B.V.