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09/20/2023
Reproducing Failures in Fault Signatures
Software often fails in the field, however reproducing and debugging fie...
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07/18/2023
An Effective Data-Driven Approach for Localizing Deep Learning Faults
Deep Learning (DL) applications are being used to solve problems in crit...
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06/13/2023
TRACED: Execution-aware Pre-training for Source Code
Most existing pre-trained language models for source code focus on learn...
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05/25/2023
Beryllium: Neural Search for Algorithm Implementations
In this paper, we explore the feasibility of finding algorithm implement...
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05/04/2023
A Study of Static Warning Cascading Tools (Experience Paper)
Static analysis is widely used for software assurance. However, static a...
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12/15/2022
An Empirical Study of Deep Learning Models for Vulnerability Detection
Deep learning (DL) models of code have recently reported great progress ...
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12/15/2022
DeepDFA: Dataflow Analysis-Guided Efficient Graph Learning for Vulnerability Detection
Deep learning-based vulnerability detection models have recently been sh...
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09/02/2022
FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
Fuzzing has been an important approach for finding bugs and vulnerabilit...
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12/07/2021
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Deep Neural Networks (DNNs) are used in a wide variety of applications. ...
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06/08/2021
Validating Static Warnings via Testing Code Fragments
Static analysis is an important approach for finding bugs and vulnerabil...
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03/04/2021
DeepLocalize: Fault Localization for Deep Neural Networks
Deep neural networks (DNNs) are becoming an integral part of most softwa...
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11/20/2019
Testing Criteria for Mobile Apps Based on Callback Sequences
App quality has been shown to be the most important indicator of app ado...
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11/18/2019
