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Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus, the quantification of the quality parameters and integrating them into the quality models is very essential. Many attempts have been made to precisely quantify the software quality parameters using various models such as Boehm's Model, McCall's Model and ISO/IEC 9126 Quality Model. A major challenge, although, is that effective quality models should consider two types of knowledge: imprecise linguistic knowledge from the experts and precise numerical knowledge from historical data.Incorporating the experts' knowledge poses a constraint on the quality model; the model has to be transparent.In this study, the authorspropose a process for developing fuzzy logic-based transparent quality prediction models. They applied the process to a case study where Mamdani fuzzy inference engine is used to predict software maintainability. Theycompared the Mamdani-based model with other machine learning approaches.The resultsshow that the Mamdani-based model is superior to all.

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A software fault is a defect that causes software failure in an executable product. Fault prediction models usually aim to predict either the probability or the density of faults that the code units contain. Many fault prediction models using software metrics have been proposed in the Software Engineering literature. This study focuses on evaluating high-performance fault predictors based on support vector machines (SVMs) and probabilistic neural networks (PNNs). Five public NASA datasets from the PROMISE repository are used to make these predictive models repeatable, refutable, and verifiable. According to the obtained results, the probabilistic neural networks generally provide the best prediction performance for most of the datasets in terms of the accuracy rate.

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