Treffer: Deep Learning Architectures for Software Fault Prediction: The Impact of Error‐Type Metrics and Class Imbalance.

Software fault prediction (SFP) plays a crucial role in modern software development by enabling early identification of fault‐prone modules and efficient allocation of testing resources. While deep learning approaches have shown promise in this domain, challenges persist regarding architectural choices, metric selection, and class imbalance issues. This study presents a comprehensive comparison between deep neural networks (DNNs) and hybrid Graph Neural Network‐Long Short‐Term Memory (GNN+LSTM) models for SFP, investigating their effectiveness when combined with both conventional software metrics and error‐type metrics. We evaluate these approaches on four real‐world Java projects: ANTLR v4, JUnit, OrientDB, and Elastic Search. Our results demonstrate that GNN+LSTM models consistently outperform traditional DNN approaches, achieving improvements of up to 4% in accuracy and 4% in F1‐score. However, we identify challenges in combining different metric sets, with performance actually degrading compared to our previous study using error‐type metrics alone, suggesting potential multi‐collinearity issues. Additionally, we examine the effectiveness of the synthetic minority oversampling technique (SMOTE) in addressing the class imbalance issue, observing improvements of up to 6.6% in accuracy for GNN+LSTM models in severely imbalanced datasets. Our findings provide practical insights for selecting appropriate model architectures and metric combinations in SFP while highlighting the importance of carefully considering feature interactions and class imbalance mitigation strategies. [ABSTRACT FROM AUTHOR]

Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)