An Explainable Machine Learning Framework for Software Reliability Prediction and Automated Technical Debt Assessment

Abstract

Software reliability forecasting and technical debt management are frequently treated as separate engineering decisions despite sharing code quality, change history, architecture and operational context. This paper develops an explainable machine learning framework that jointly estimates defect-proneness and future incident risk while producing a traceable technical debt priority score. The proposed pipeline integrates repository commits, issue records, static analysis measures, test coverage, dependency topology and deployment telemetry; enforces time-aware validation; calibrates risk estimates; and uses SHAP-style feature attributions with counterfactual checks. A cost-aware ranking combines expected reliability loss, estimated remediation effort, interest accumulation and uncertainty. A synthetic worked example illustrates the reporting protocol, including discrimination, calibration, debt prioritization and ablation. These numerical results are demonstrative, not empirical claims. The framework emphasizes data provenance, human review, model drift and reproducible evaluation, enabling engineering teams to connect model explanations to actionable maintenance decisions.