Fault localization
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Fault localization
Most implemented papers
Dynamic Neural Program Embedding for Program Repair
keowang/dynamic-program-embedding
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Evaluation results show that our new semantic program embedding significantly outperforms the syntactic program embeddings based on token sequences and abstract syntax trees.
Explaining Image Classifiers using Statistical Fault Localization
The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for "Explainable AI".
Neural Attribution for Semantic Bug-Localization in Student Programs
In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program.
NNrepair: Constraint-based Repair of Neural Network Classifiers
We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class.
An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery.
An exact counterfactual-example-based approach to tree-ensemble models interpretability
And the black-boxes approaches, which are used to explain such model decisions, suffer from a lack of accuracy in tracing back the exact cause of a model decision regarding a given input.
AequeVox: Automated Fairness Testing of Speech Recognition Systems
AequeVox simulates different environments to assess the effectiveness of ASR systems for different populations.
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Also, it can provide actionable insights for fix whereas DeepLocalize can only report faults that lead to numerical errors during training.
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Besides, for fault localization, DeepFD also outperforms the existing works, correctly locating 42% faulty programs, which almost doubles the best result (23%) achieved by the existing works.
FedDebug: Systematic Debugging for Federated Learning Applications
SEED-VT/FedDebug
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FedDebug's interactive debugging incurs 1. 2% overhead during training, while it localizes a faulty client in only 2. 1% of a round's training time.
