# DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks

arXiv: https://arxiv.org/abs/2410.19550 · HTML: https://mlsanity.com/paper/2410.19550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://mlsanity.com/paper/1908.04942/) (2019)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction](https://mlsanity.com/paper/1802.04986/) (2018)
- [Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering](https://mlsanity.com/paper/2202.06146/) (2022)
- [The Impact of Using Regression Models to Build Defect Classifiers](https://mlsanity.com/paper/2202.06157/) (2022)
- [Benchmarking Machine Learning Technologies for Software Defect Detection](https://mlsanity.com/paper/1506.07563/) (2015)
- [Towards Effective Bug Triage with Towards Effective Bug Triage with Software Data Reduction Techniques](https://mlsanity.com/paper/1704.04761/) (2017)
- [Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction](https://mlsanity.com/paper/2202.06145/) (2022)

## Abstract (arXiv)

Software defect prediction (SDP) aims to identify high-risk defect modules in software development, optimizing resource allocation. While previous studies show that dependency network metrics improve defect prediction, most methods focus on code-based dependency graphs, overlooking developer factors. Current metrics, based on handcrafted features like ego and global network metrics, fail to fully capture defect-related information. To address this, we propose DeMuVGN, a defect prediction model that learns multi-view software dependency via graph neural networks. We introduce a Multi-view Software Dependency Graph (MSDG) that integrates data, call, and developer dependencies. DeMuVGN also leverages the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance defect module identification. In a case study of eight open-source projects across 20 versions, DeMuVGN demonstrates significant improvements: i) models based on multi-view graphs improve F1 scores by 11.1% to 12.1% over single-view models; ii) DeMuVGN improves F1 scores by 17.4% to 45.8% in within-project contexts and by 17.9% to 41.0% in cross-project contexts. Additionally, DeMuVGN excels in software evolution, showing more improvement in later-stage software versions. Its strong performance across different projects highlights its generalizability. We recommend future research focus on multi-view dependency graphs for defect prediction in both mature and newly developed projects.