Machine Learning-Based Prediction of Technical Debt in Continuous Software Development Using Repository Mining and Developer Activity Analytics
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P122Keywords:
Technical Debt Prediction, Repository Mining, Developer Analytics, Machine Learning, Continuous Software Development, Self-Admitted Technical Debt, Software Maintenance, CI/CD Risk, Explainable AIAbstract
Technical debt is no longer an occasional by-product of rushed implementation; it has become a persistent socio-technical risk in continuous software development environments where code, build scripts, configuration artifacts, tests, issue tickets, pull requests, and deployment workflows evolve at high velocity. Conventional technical debt management methods often depend on manual code reviews, static analysis alerts, or retrospective refactoring decisions, which are insufficient for detecting emerging debt before it becomes costly to repay. This paper proposes a machine learning-based framework for predicting technical debt in continuous software development by integrating repository mining, developer activity analytics, change-process metrics, self-admitted technical debt signals, and software lifecycle governance indicators. The proposed framework models technical debt as an evolving risk state rather than a static code smell, combining structural code metrics, commit-level churn, ownership dispersion, pull-request review behavior, issue-tracker semantics, CI/CD instability, and developer workload signals. The study introduces a multi-source feature architecture, a temporal labeling strategy, an explainable ensemble learning pipeline, and a debt-prioritization layer designed for continuous integration environments. Unlike review-oriented approaches that summarize technical debt literature, this manuscript develops a research-oriented predictive design that can be operationalized in modern software engineering pipelines. The expected contribution is a decision-support mechanism that helps engineering teams identify debt-prone modules, anticipate repayment urgency, improve sprint planning, and align refactoring decisions with delivery risk, developer capacity, and long-term maintainability.
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