Abstract— In academics and industry, software bug prediction (SBP) is essential for assessing worker dependability. Early fault discovery enhances software adaption, efficacy, user happiness, and resource efficiency. Early in the software development lifecycle, a variety of measurements and techniques are used. The goal is to enhance the accuracy, recall, and precision of software problem detection in retrieving relevant flaws. By merging FPRNN with Hyperbolic Mapping, the FPRNN-HM approach improves software defect prediction by speeding up convergence and enhancing searching power, ultimately identifying ideal attributes. The FPRNN-HM model achieves high accuracy of 98.45% for big datasets, prevents overfitting, and offers high computation, making it an affordable tool for software development bug prediction.

Keywords— SBP, FPRNN-HM, Accuracy, Precision

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