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07/24/2023
MorphStream: Scalable Processing of Transactions over Streams on Multicores
Transactional Stream Processing Engines (TSPEs) form the backbone of mod...
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07/04/2023
Equivalence, Identity, and Unitarity Checking in Black-Box Testing of Quantum Programs
Quantum programs exhibit inherent non-deterministic behavior, which pose...
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06/30/2023
Testing Multi-Subroutine Quantum Programs: From Unit Testing to Integration Testing
Quantum computing has emerged as a promising field with the potential to...
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06/18/2023
On Refactoring Quantum Programs
Refactoring is a crucial technique for improving the efficiency and main...
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06/10/2023
ScaffML: A Quantum Behavioral Interface Specification Language for Scaffold
Ensuring the correctness of quantum programs is crucial for quantum soft...
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06/10/2023
An Empirical Study of Bugs in Quantum Machine Learning Frameworks
Quantum computing has emerged as a promising domain for the machine lear...
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04/12/2023
AutoRepair: Automated Repair for AI-Enabled Cyber-Physical Systems under Safety-Critical Conditions
Cyber-Physical Systems (CPS) have been widely deployed in safety-critica...
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04/11/2023
Static Entanglement Analysis of Quantum Programs
Quantum entanglement plays a crucial role in quantum computing. Entangli...
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04/10/2023
QChecker: Detecting Bugs in Quantum Programs via Static Analysis
Static analysis is the process of analyzing software code without execut...
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03/25/2023
Formalization of Quantum Intermediate Representations for Code Safety
Quantum Intermediate Representation (QIR) is a Microsoft-developed, LLVM...
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03/13/2023
Boosting Source Code Learning with Data Augmentation: An Empirical Study
The next era of program understanding is being propelled by the use of m...
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01/27/2023
Neural Episodic Control with State Abstraction
Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample...
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12/27/2022
Towards Benchmarking GUI Compatibility Testing on Mobile Applications
GUI is a bridge connecting user and application. Existing GUI testing ta...
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10/06/2022
Enhancing Code Classification by Mixup-Based Data Augmentation
Recently, deep neural networks (DNNs) have been widely applied in progra...
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10/06/2022
Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling
Graph neural networks (GNNs) have recently been popular in natural langu...
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10/06/2022
AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
While the ability to build quantum computers is improving dramatically, ...
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08/19/2022
Testing Quantum Programs with Multiple Subroutines
Errors in quantum programs are challenging to track down due to the unce...
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01/21/2022
A Comprehensive Study of Bug Fixes in Quantum Programs
As quantum programming evolves, more and more quantum programming langua...
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11/24/2021
Machine Learning Guided Cross-Contract Fuzzing
Smart contract transactions are increasingly interleaved by cross-contra...
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08/22/2021
Bugs4Q: A Benchmark of Real Bugs for Quantum Programs
Realistic benchmarks of reproducible bugs and fixes are vital to good ex...
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03/16/2021
Identifying Bug Patterns in Quantum Programs
Bug patterns are erroneous code idioms or bad coding practices that have...
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03/16/2021
Some Size and Structure Metrics for Quantum Software
Quantum software plays a critical role in exploiting the full potential ...
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07/14/2020
Quantum Software Engineering: Landscapes and Horizons
Quantum software plays a critical role in exploiting the full potential ...
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06/13/2020
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
As the GAN-based face image and video generation techniques, widely know...
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04/24/2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Over the past decade, deep learning (DL) has been successfully applied t...
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09/15/2019
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Deep Learning (DL) has recently achieved tremendous success. A variety o...
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12/13/2018
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Deep learning (DL) defines a data-driven programming paradigm that autom...
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11/13/2018
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Deep learning (DL) has recently achieved tremendous success in a variety...
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10/10/2018
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Over the past decades, deep learning (DL) systems have achieved tremendo...
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09/04/2018
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
In company with the data explosion over the past decade, deep neural net...
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09/04/2018
Coverage-Guided Fuzzing for Deep Neural Networks
In company with the data explosion over the past decade, deep neural net...
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06/20/2018
Combinatorial Testing for Deep Learning Systems
Deep learning (DL) has achieved remarkable progress over the past decade...
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05/14/2018
DeepMutation: Mutation Testing of Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm where ...
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03/20/2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning defines a new data-driven programming paradigm that constr...
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03/20/2018
