TOWARDS AN ADAPTIVE COST-OPTIMIZATION FRAMEWORK FOR SOFTWARE MAINTENANCE: INTEGRATING PREDICTIVE MODELING WITH ORGANIZATIONAL READINESS - Siddiqi, Ahmed Masih Uddin; Varshney, Manoj (ShodhKosh: Journal of Visual and Performing Arts, 2026)
It imparts no less than 60 to 90 percent of total software lifetime costs on software maintenance but usage of predictive maintenance models in practice is infrequent. Past research has already defined two coordination findings: First, machine learning-based models of effort (such as Predictive Maintenance Effort Model (PMEM)) are capable of reducing resource misallocation by over 95 percent relative to the reactive approaches; and second, technical and organizational factors and obstacles machine learning based models have included inadequate data on defects, insufficient documentation, reliance on tacit knowledge, and resistance to Nevertheless, the predictive accuracy is not integrated with the organizational readiness as a requirement to deployment as a unified framework. The following paper presents a proposal of the Adaptive Cost-Optimization Framework (ACOF), a three-layer architecture, which includes a quantified Readiness Assessment Module, an adaptive Predictive Effort Engine that builds on the capabilities of PMEM and a continuous feedback loop, and a Governance and Monitoring Layer capable of being integrated with DevOps. The study of ACOF is assessed by simulating projects on a low, medium and high readiness level. Findings show that ACOF decreases even more the Sum of Squared Errors misallocation proxy than baseline PMEM and the greatest benefits are found in high-readiness settings. The framework offers an actionable pathway that these organizations and in particular the Small-to-Medium Enterprises (SMEs) can adopt to transform the reactive-based maintenance expenditure to be an informed cost governance.