Treffer: MetaGAN: Metamorphic GAN-Based Augmentation for Improving Deep Learning-Based Multiple-Fault Localization Without Test Oracles.

Modern electronic information system software is becoming increasingly complex, making manual debugging prohibitively expensive and necessitating automated fault localization (FL) methods to prioritize suspicious code segments. While Single-Fault Localization (SFL) methods, such as spectrum-based fault localization (SBFL) and Deep Learning-Based Fault Localization (DLFL), have demonstrated promising results in localizing individual faults, extending these methods to multiple-fault scenarios remains challenging. Deep Learning–Based Fault Localization (DLFL) methods combine metamorphic testing and clustering to locate multiple faults without relying on test oracles. However, these approaches suffer from a severe class imbalance problem: the number of failed cases (the minority class) is far smaller than that of passed cases (the majority class). To address this issue, we propose MetaGAN: Metamorphic GAN-based Augmentation for Improving Deep Learning-based Multiple-Fault Localization Without Test Oracles. MetaGAN is a novel method that integrates Metamorphic Testing (MT), clustering-based fault isolation, and Generative Adversarial Networks (GANs). The method first utilizes MT to gather information from failed Metamorphic Test Groups (MTGs) and extracts metamorphic features that capture the underlying failure causes to represent each failed MTG; then, these features are used to cluster the failed MTGs into several groups, with each group forming an independent single-fault debugging session; finally, in each session, data augmentation is performed by combining MT with a GAN model to generate failed test cases (the minority class) until their number matches that of passed test cases (the majority class), thereby balancing the dataset for precise DLFL-based fault localization and enabling parallel debugging of multiple faults. Extensive experimental validation on an expanded open-source benchmark shows that, compared with the baseline MetaMDFL, MetaGAN significantly improves fault localization accuracy, particularly in parallel multiple-fault scenarios. Specifically, MetaGAN achieves significant improvements in both the EXAM and the rank metrics, with EXAM showing the highest improvement of 7.81 % , the rank showing the highest improvement of 12.71 % , and the top- N % showing the highest improvement of 9.62 % . This method, through coordinated dynamic feature extraction, adaptive data augmentation, and distributed collaborative debugging, provides a scalable solution for complex systems where test oracles are unavailable, thereby advancing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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