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Mutants Will Tell: Statistical Mutation-Based Multiple Fault Localization for Deep Learning Programs
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AI
DOI：10.1109/TSE.2026.3655800.png)
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Abstract
En 中文
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: Statistical Mutation-based Multiple Fault Localization 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.
Keywords:
Location awareness
Data models
Computer bugs
Source coding
Feature extraction
Deep learning
Unified modeling language
Fault diagnosis
Data processing
Artificial neural networks
Fault localization
deep learning fault
mutation analysis
probability model
suspiciousness evaluation
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