Fuzzy Adaptive Logic Networks as Hybrid Models of Quantitative Software Engineering

Abstract Quantitative software engineering is aimed at designing models describing software processes and products. While being noticeably dominated by statistical regression models, this area also embraces advanced techniques of computational intelligence and knowledge-based engineering including rule-based models, fuzzy models, and neural networks. The rationale behind their usage in the setting of Software Engineering is threefold: (a) the underlying distributions of datasets may not adhere to general assumptions that are usually made in the setting of linear regression models: (b) it is beneficial to build interpretable models that in some sense are user-friendly; (c) the models should be advanced and computationally appealing so that they exhibit some nonlinear characteristics as well as are fully equipped with learning abilities. The proposed architecture comprises alogic-based skeleton (blueprint) and an array of generic perceptrons as being commonly encountered in neurocomputing. The hybrid nature...