Jiang Wu

Orcid: 0000-0002-6353-8890

Affiliations:
  • National University of Defense Technology, College of Computer, Changsha, China


According to our database1, Jiang Wu authored at least 16 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Debug Like a Human: Scaling LLM-based Fault Localization to Processor Design via Block-Level Instruction-Oriented Slicing.
CoRR, May, 2026

SRepair: Symbolic Regression-Based Repair for Hardware Design Code.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., March, 2026

2025
Rtl design flaws revisited: a data-driven study of systematic bug patterns in Verilog code.
J. Supercomput., September, 2025

2024
An effective fault localization approach for Verilog based on enhanced contexts.
Frontiers Comput. Sci., October, 2024

Time-Aware Spectrum-Based Bug Localization for Hardware Design Code with Data Purification.
ACM Trans. Archit. Code Optim., September, 2024

Knowledge-Augmented Mutation-Based Bug Localization for Hardware Design Code.
ACM Trans. Archit. Code Optim., September, 2024

Strider: Signal Value Transition-Guided Defect Repair for HDL Programming Assignments.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., May, 2024

Simple but Powerful Beginning: Metamorphic Verification Framework for Cryptographic Hardware Design.
Proceedings of the 30th IEEE International Conference on Parallel and Distributed Systems, 2024

2023
Validating the Redundancy Assumption for HDL from Code Clone's Perspective.
Proceedings of the 2023 International Symposium on Physical Design, 2023

Mantra: Mutation Testing of Hardware Design Code Based on Real Bugs.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

2022
Fault Localization for Hardware Design Code with Time-Aware Program Spectrum.
Proceedings of the IEEE 40th International Conference on Computer Design, 2022

2020
Enabling Reliability-Driven Optimization Selection with Gate Graph Attention Neural Network.
Int. J. Softw. Eng. Knowl. Eng., 2020

A Highly Reliable Compilation Optimization Passes Sequence Generation Framework.
IEICE Trans. Inf. Syst., 2020

Compilation Optimization Pass Selection Using Gate Graph Attention Neural Network for Reliability Improvement.
IEEE Access, 2020

Reliable Compilation Optimization Phase-ordering Exploration with Reinforcement Learning.
Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

High-Reliability Compilation Optimization Sequence Generation Framework Based ANN.
Proceedings of the 20th IEEE International Conference on Software Quality, 2020


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