Devising a cross-project learning method for software defect prediction based on fuzzy algorithmic models - Rakytyanska, Hanna; Prus, Bohdan (Eastern-European Journal of Enterprise Technologies, 2026)
This work investigates fuzzy algorithmic models for predicting software defects. An issue related to conventional machine (deep) learning models is complexity and interpretability. In addition, cross-project learning of such models requires solving the problem of heterogeneity in the initial and target data distributions. A fuzzy algorithmic model for predicting software defects with an integrated distribution of implementation options for development stages has been proposed. A genetic-neural method for tuning a fuzzy algorithmic model to cross-project data has been devised. Unlike machine learning models, the interpretability of the prediction model was achieved by assessing the correctness (defectivity) of development stages using fuzzy rules. The model is built on the basis of “work-control-refinement” algorithmic structures, which is an analog of a fuzzy knowledge base. Fuzzy estimates of the defectivity of the execution of work, control, and refinement operators are subject to tuning, where defect ranks model the distribution of resources. The training sample is formed on the basis of estimates of the correctness of implementation options for algorithmic structures. The integration of indicators for implementation options for work, control, and refinement operators has made it possible to solve the issue of heterogeneity in the initial and target data distributions. Unlike known methods for training neural-fuzzy models of software reliability, simplification of the tuning process was achieved by transferring cross-project data. The scope of practical application includes predicting the quality of new software based on the experience of completed projects. The condition of use is a discrete algorithmic model of the development process