Hybrid CNN–LSTM Models for Cross-Project Fault Prediction: Robust Generalization Under Dataset Shift
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P133Keywords:
Cross-Project Fault Prediction, Dataset Shift, Domain Generalization, Hybrid Deep Learning, CNN, LSTM, Software Defect Prediction, Ci/Cd Analytics, Robustness, Explainable AIAbstract
Abstract: Cross-project fault prediction (CPFP) aims to learn defect predictors from one or more source projects and deploy them on a different target project where labels may be unavailable, incomplete, delayed, or noisy. Despite decades of progress in software defect prediction, generalization in CPFP remains brittle because target projects commonly exhibit dataset shift: changes in metric distributions, process behaviors, architectural patterns, operational platforms, and data pipelines that violate the implicit i.i.d. assumptions of standard supervised learning. This manuscript proposes a shift-aware hybrid CNN–LSTM framework tailored to CPFP under realistic dataset shift. The model combines convolutional feature extractors (for local, interaction-level patterns among engineered metrics or change descriptors) with recurrent sequence modeling (for temporal and cross-change dependencies), and integrates training strategies that stabilize performance under covariate and conditional shift. We formalize the CPFP learning objective under shift, define a practical shift taxonomy grounded in modern enterprise software contexts, and introduce a training pipeline using robust scaling, project-conditional normalization, and shift-mitigation regularization. The approach is designed for operational feasibility: it supports constrained edge environments and embedded telemetry streams, aligns with CI/CD governance processes, and incorporates explainability requirements for high-stakes domains. A worked example illustrates how dataset shift can invert model rankings across projects and how shift-aware training reduces this volatility. The manuscript concludes with a reproducible evaluation protocol, threat analysis, and deployment guidance for fault prediction in cloud-native and regulated settings.
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