Context: The detection of code defects is foundational to modern software development and maintenance, playing a critical role in ensuring software quality and security. However, as software systems grow in scale and complexity, the limitations of traditional static analysis and conventional machine learning techniques have become increasingly evident. These methods rely heavily on intricate, manual feature engineering and fail to capture dynamic runtime behavior, resulting in suboptimal accuracy and elevated error rates. Objective: To address these deficiencies, we propose UF-CDDFM, a unified framework for code defect detection that integrates multi-modal inputs, active learning, and state-of-the-art few-shot learning techniques. We aim to improve detection performance, reduce feature selection complexity and sample bias through active learning, and maintain practical efficiency in real-world development contexts. Methods: UF-CDDFM employs parallel encoding of source code, code annotations, and abstract syntax trees (ASTs) using large language models (LLMs) alongside multilayer perceptrons (MLPs) to derive robust, high-fidelity representations of code. To streamline feature selection and mitigate sample bias, an active learning component is introduced for automated identification of high-quality features. Addressing the pervasive challenge of data scarcity, we incorporate two complementary few-shot learning strategies-MAML for small-scale datasets and LEO for larger-scale settings to enhance overall generalization capability. Results: Empirical evaluations demonstrate that UF-CDDFM consistently outperforms existing methods, establishing new state-of-the-art detection rates: 72.04% for defect detection and 95.23% for clone detection. Crucially, these gains are achieved within resource-constrained computational environments, which highlights the practicality of the method. Conclusion: By fusing multi-modal code representations, active learning, and adaptive few-shot learning techniques, UF-CDDFM delivers significant improvements in detection accuracy and computational efficiency. This work offers a new paradigm for robust, scalable, and practical code defect and clone detection in modern software engineering.

Cross-project defect prediction (CPDP) is hindered by distribution shifts between source and target projects, so models that excel in within-project software defect prediction (WPDP) often degrade across projects. We propose FLDP, which couples (i) local subset alignment selecting similar source-target file pairs via three file-level metrics and aligning only those subsets with (ii) sequence-graph feature fusion, where TLSTM encodes token sequences and TGCN encodes AST structure into a unified representation. Across 10 transfers on 7 projects, FLDP consistently outperforms classical and recent CPDP baselines in AUC/F1/MCC. Ablation shows both local alignment and fusion are necessary for the gains, and our analysis of selection metrics offers practical guidance for applying CPDP in heterogeneous settings.