How can logistic regression improve software reliability? This paper explores the use of logistic regression in classifying software modules as fault-prone or not, guiding development processes to focus resources on those parts most likely to have faults. A key contribution is applying prior probabilities and misclassification costs to a logistic regression-based classification rule for a software quality model. This approach offers advantages over other classification techniques, such as interpretable coefficients. The paper presents an integrated method for using logistic regression in software quality modeling, illustrating the interpretation of coefficients and the use of prior probabilities and costs of misclassifications. A case study of a major subsystem in a military, real-time system demonstrates these techniques, providing practical insights into enhancing software quality through targeted analysis and resource allocation.
This paper aligns with the International Journal of Reliability, Quality and Safety Engineering, as it directly addresses methods for improving software quality and reliability. The application of logistic regression in software modeling enhances the journal's coverage of techniques for ensuring robustness and safety in engineering systems. The case study provides practical insights relevant to the journal's readership.