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Youshu Ji

dblp:261/9268 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Electronic design automation · 100%
Software engineering, system software, and programming languages
2 papers
Program synthesis and code generation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › hardware verification and test › hardware verification › assertion-based verification
assertion failure debugging
1.722025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Electronic design automation › hardware verification and test
hardware verification
1.722025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Electronic design automation › hardware verification and test › functional verification
RTL verification
1.722025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Program synthesis and code generation
code generation with language models
0.522025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design · DAC 2025

Methods — techniques the papers use, named apart from their topics

synthetic training data · 3.5large language model · 3.5error response learning · 3.5
YearPublicationVenuePosition
2025 Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design
abstract
SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of 88.54% on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.
Jie Zhou 0001, Youshu Ji, Ning Wang 0071, Xinyao Jiao, Bingkun Yao, Xinwei Fang, Shuai Zhao 0004, Nan Guan, Zhe Jiang 0004
DAC2
2025 Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design
abstract
SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of $88.54 \%$ on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.
Jie Zhou 0001, Youshu Ji, Ning Wang 0071, Xinyao Jiao, Bingkun Yao, Xinwei Fang, Shuai Zhao 0004, Nan Guan, Zhe Jiang 0004
DAC2