Enhancing the GLANCE Framework for Line-Level Defect Prediction: An Empirical Study of Semantically-Aware Metrics and Non-Linear Classifiers - Zahid Mujaddid; Ema Utami (Universitas Amikom Purwokerto, 2025)
Line-level defect prediction (LLDP) is critical for reducing software maintenance costs, yet its industrial adoption is often hindered by high false alarm rates that erode developer trust. While the state-of-the-art GLANCE-LR framework offers a lightweight solution, it relies on linear classifiers and purely syntactic heuristics, failing to capture the non-linear defect patterns and semantic risks associated with complex code constructs. To bridge the gap between operational efficiency and semantic awareness, this paper proposes GLANCE++, an enhanced framework that integrates a non-linear LightGBM classifier for refined file-level filtering and introduces three semantically-aware line metrics: Cognitive Complexity Score (CCS), API-Weighted Number of Function Calls (AW-NFC), and Variable-Write Count (VWC). These metrics shift the prediction paradigm from counting tokens to modeling