Multi-Objective Fault Prediction for Agile Teams: Balancing Precision, Recall, and Planning Cost in Sprint Backlog Decisions
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P132Keywords:
Agile Software Development, Defect Prediction, Multi-Objective Optimization, Sprint Planning, Precision-Recall Trade-Off, Cost-Sensitive Learning, Decision Intelligence, Explainable AIAbstract
Agile teams increasingly rely on machine learning based fault prediction to anticipate risky backlog items and allocate quality effort within short sprint horizons. However, practical adoption remains limited because most fault prediction studies optimize a single model metric rather than the decision an agile team must make during sprint planning: which subset of backlog items should receive additional testing, review, refactoring, or operational safeguards under finite sprint capacity. This paper formulates sprint backlog quality planning as a multi-objective decision problem that explicitly balances (i) precision, to avoid wasting limited capacity on false alarms, (ii) recall, to avoid missing high-risk items that become escaped defects, and (iii) planning cost, to reflect the opportunity cost of quality actions within a sprint. We propose ParetoSprint, a decision-support pipeline that (1) learns fault probabilities from historical engineering signals, (2) calibrates uncertainty for sprint-scale decision thresholding, (3) models per-item planning cost using story-point aligned effort proxies and operational constraints, and (4) generates a Pareto front of sprint-quality plans using an elitist multi-objective evolutionary search. The approach provides agile teams with multiple actionable options rather than a single threshold, enabling explicit trade-off selection consistent with business priorities and delivery risk. A worked sprint example illustrates how ParetoSprint can shift sprint planning from ad hoc risk discussions to reproducible, auditable, and explainable decisions aligned with engineering governance.
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