As deep learning (DL) systems are increasingly deployed in safety-critical domains, e.g., intelligent planning and autonomous driving, localizing faults that occur in such systems becomes indispensable. Inevitably, DL systems also suffer from faults like traditional software. Although single fault localization for DL programs has been studied, the multiple-fault localization for DL programs remains underexplored. We notice that mutation analysis is a powerful technique for locating multiple faults since it can simulate the faulty behaviors of a DL program by generating multiple mutants simultaneously. Thus, we propose MuMuFL: StatisticalMutation-basedMultipleFaultLocalization approach to locate the multiple faulty statements residing in a faulty DL program. The insight of MuMuFL is that the different behaviors of mutants provide valuable information for pinpointing the faulty statements of a DL fault. MuMuFL defines and leverages DL mutation operators on a DL program to simulate the faulty DL behavior. Then, MuMuFL evaluates the difference in the accuracy between the original DL model and the mutated DL model to quantify the suspiciousness of each statement being faulty. Finally, the large-scale experiments show that MuMuFL effectively localizes DL faults, e.g., localizing 36% of multiple-fault DL programs, whereas the best-performing baseline can only localize 14% of them.

A test suite is indispensable for fault localization by providing useful execution information of its test cases for locating suspicious statements of being faulty. There exists a type of test cases known as coincidental correctness (CC) test cases, which executes the faulty statement whereas produces the anticipated output. The existing studies have shown CC test cases harmfully impact fault localization effectiveness. Therefore, it is crucial to detect CC test cases to mitigate the adverse impact of CC test cases on fault localization.To address this issue, we propose ContraCC: a CC test cases detection method using contrastive learning. The insight of ContraCC is that the internal structural information of source test case execution data should be beneficial for CC detection whereas there is a lack of suitable representation methods. Inspired by the insight, ContraCC uses contrastive learning to learn new differentiated representations as test case vectors, which differentiate between similar and dissimilar pairs of test cases by maximizing their similarity within the same class and minimizing it between different classes. Based on the contrastive learning representations (i.e., test case vectors), ContraCC adopts multi-layer perceptron for binary classification to detect CC in downstream tasks. To evaluate the effectiveness of ContraCC, we conduct large-scale experiments on widely-used benchmarks by comparing ContraCC with five state-of-the-art CC test cases detection methods and applying ContraCC for fault localization. The experimental results show that ContraCC outperforms four state-of-the-art methods (e.g., from 10% to 84% improvement in Top-N on the best-performing baseline NeuralCCD) and significantly improves fault localization effectiveness (e.g., 24% improvement on the best-performing baseline Dstar).