LLM-Driven Software/Hardware Code Generation, Testing, and Analysis
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 December 2026 | Viewed by 13
Special Issue Editors
Interests: code generation and optimization; software quality and security; program comprehension and mining
Special Issue Information
Dear Colleagues,
Large language models (LLMs) are fundamentally transforming how we create, verify, and understand code—first in software engineering and now, increasingly, in the design of digital hardware. This Special Issue aims to capture the convergence of these two domains by focusing on LLM-driven techniques that automate code generation, testing, and analysis for both software and hardware description languages (HDLs). We seek to establish a unified platform where advances in AI for software engineering (AI4SE) inform hardware design automation, and where the stringent correctness demands of hardware challenge and refine LLM-based methods.
The topical collection centers on the intersection of LLMs, software/hardware code engineering, and the full lifecycle of development.
The core focus is on LLM-powered automation that treats software and hardware code through a common software engineering lens—encompassing general-purpose programming languages (e.g., Python, C++) and HDLs (e.g., Verilog, VHDL, SystemVerilog) alike.
We invite contributions across three tightly interwoven areas:
(1) Code Generation: From natural-language specifications or high-level models to both executable software and synthesizable hardware;
(2) Testing and Verification: Automated testbench creation, assertion generation, coverage-driven verification, and bug detection/repair for software and hardware artifacts;
(3) Analysis: Static and dynamic quality assessment, readability, performance/power/timing estimation, design-rule checking, and program comprehension.
Submissions may introduce novel models, fine-tuning strategies, benchmarks, datasets, tool chains, empirical studies, or critical position papers.
The Special Issue aims to consolidate pioneering research that narrows the gap between LLM-based software engineering and electronic design automation. By co-locating software and hardware perspectives, we intend to foster cross-pollination, identify shared challenges (e.g., functional correctness, scalability, trust), and accelerate the adoption of LLMs in safety-critical code domains.
We warmly invite you to submit your original research or review articles.
Dr. Guang Yang
Dr. Xiang Chen
Guest Editors
Manuscript Submission Information
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Keywords
- large language models (LLMs)
- code generation
- software testing
- program analysis
- hardware description language (HDL)
- Verilog
- software/hardware co-design
- AI for software engineering (AI4SE)
- electronic design automation (EDA)
- deep learning
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