EXPLAINABLE RELEASE RISK FORECASTING IN ENTERPRISE CI/CD PIPELINES BASED ON REPOSITORY SIGNALS, TEST HISTORIES, AND BUILD STABILITY INDICATORS

Abstract

Release risk forecasting has become one of the most consequential analytical tasks in contemporary enterprise software engineering because modern delivery systems no longer depend on isolated code snapshots evaluated shortly before deployment. They depend on continuous integration and continuous delivery pipelines in which source changes, build outcomes, test traces, review activity, infrastructure conditions, and release decisions interact at high frequency. Recent systematic work on artificial intelligence in software testing shows that software-quality evaluation is increasingly moving toward richer metrics that account not only for accuracy but also for operational impact, risk sensitivity, and the completeness of testing processes. At the same time, studies on build optimization and continuous-integration failure prediction indicate that build failures are associated with factors such as code complexity, dependency conditions, test behavior, and historical instability, while industrial work on test-failure prediction shows that historical test repositories and explainable AI can be used to provide actionable feedback to stakeholders in real deployment contexts. These developments suggest that enterprise release risk should not be treated as a narrow post-development quality gate but as an evolving property of the software-delivery pipeline itself.

This article examines explainable release risk forecasting in enterprise CI/CD environments through the combined use of repository signals, test histories, and build-stability indicators. The study interprets release risk not as a single probability of failure, but as a compound operational condition shaped by structural code burden, change intensity, build fragility, test instability, historical defect manifestation, and decision compression near release boundaries. Explainability is treated as a core requirement because release decisions in enterprise contexts affect testing effort, rollback planning, deployment gating, and managerial accountability. A forecasting model that cannot justify why a release candidate appears risky may provide signals, but it does not provide governance. The first block of the study therefore establishes the technological and organizational foundations of the problem, clarifies why enterprise CI/CD pipelines produce an especially rich basis for release-risk analysis, and argues that hybrid signal architectures are better suited to real release governance than code-only or build-only models.