Problem
Research questions and friction points this paper is trying to address.
software defect prediction
class imbalance
class overlap
training dynamics
data-quality issues
Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues
🤖 AI Summary
This study addresses the coupled challenges of class imbalance and class overlap in software defect prediction, which jointly impair model training dynamics and performance. The authors propose the first interaction-aware protocol for analyzing training dynamics under these intertwined data quality issues. By training a fixed multilayer perceptron (MLP) under three conditions—imbalance only, overlap only, and their coupling—the protocol systematically records training trajectories. Integrating effect size analysis, sensitivity analysis, and rule-based classification, it constructs a taxonomy of training dynamic patterns. The work uncovers distinctive neural network behaviors specific to the coupled scenario, offering empirical insights and novel diagnostic tools to enhance the understanding, evaluation, and refinement of defect prediction models.
📝 Abstract
Context: Software defect prediction supports maintenance decisions such as testing prioritization, release-risk assessment, and quality monitoring. However, metric-based SDP datasets often contain coupled data-quality issues, especially class imbalance and class overlap. Prior work has mainly measured their impact through endpoint performance, while recent evidence suggests that such issues may also appear in neural training dynamics (gradients, weights, biases, error trajectories). However, these studies examine issues in isolation, leaving open how internal neural network training patterns manifest when data quality issues are coupled.
Objective: We investigate how training-dynamics patterns from class imbalance, overlap, and their coupling can be characterized under interaction-aware conditions in deep learning-based SDP.
Method: We conduct a controlled intervention study on class-level UBD datasets, training a fixed MLP under imbalance-only, overlap-only, and joint conditions across five seeds. Training dynamics are logged per epoch; fidelity is monitored via coupling ratios. Patterns are characterized using effect sizes, trajectories, sensitivity analyses, and rule-based classification.
Expected contribution: The study will produce an interaction-aware empirical protocol and a candidate taxonomy of training-dynamics patterns for coupled data-quality issues in metric-based SDP.
Innovation
Methods, ideas, or system contributions that make the work stand out.
training dynamics
class imbalance
class overlap
coupled data-quality issues
software defect prediction
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