Scalable Adaptive Learning for Early Software Fault Prediction in Agile: From Reliability Gains to Sprint Planning Optimization

Agile teams increasingly deliver software through rapid iterations, cloud-native deployment, and continuously evolving architectures. While this cadence accelerates value delivery, it also amplifies the operational cost of faults: late discovery increases rework, destabilizes sprint commitments, and elevates reliability risk. Traditional software defect prediction has produced strong results in offline, within-project settings, yet common limitations remain for modern Agile delivery: models degrade under concept drift, labels arrive late (often after release), and predictions are rarely integrated into sprint planning decisions in a decision-traceable manner. This manuscript proposes Scalable Adaptive Learning for Sprint Analytics (SALSA), a framework that couples adaptive fault prediction with sprint planning optimization. SALSA unifies drift-aware continual learning, transfer and federated strategies for low-data or privacy-constrained contexts, effort-aware evaluation aligned with Agile quality assurance capacity, and interpretable risk rationales suitable for governance in regulated environments. The framework extends beyond probability of fault to estimate reliability gain and to optimize sprint backlog selection under capacity constraints, balancing feature delivery with risk reduction. A worked sprint planning example demonstrates how predicted risk, effort, and expected reliability gain can be translated into a concrete backlog recommendation and quality budget allocation. The manuscript also outlines a replication-ready evaluation protocol emphasizing time-ordered validation, cost-effectiveness, and measurable planning outcomes. By connecting early fault prediction to sprint-level decision making, SALSA reframes defect prediction as an operational capability that improves both software reliability and sprint planning efficiency.