As the capabilities of large models rapidly evolve from mere "content generation" to the "execution of software engineering tasks," they are becoming a crucial technological foundation for intelligent software development, quality assurance, and engineering governance. This shift propels software engineering beyond simple tool-assisted workflows toward model-enhanced processes, human-AI collaboration, and full-lifecycle intelligence. Addressing the realities of industrial software—characterized by complex requirements, diverse application scenarios, high system coupling, and stringent reliability and safety standards—large models must not only boost engineering efficiency in phases such as requirements analysis, architectural design, code generation, and documentation, but also enhance software quality and operational trustworthiness in areas like test generation, defect localization, reliability analysis, runtime monitoring, and fault diagnosis. Concurrently, as large models become deeply embedded in industrial software engineering workflows, issues such as model hallucinations, code security, intellectual property rights, version evolution, compliance auditing, liability tracing, and security governance are becoming increasingly prominent. Conducting research into key technologies—including large-model-driven software development, quality assurance, and engineering governance—holds significant academic and practical value for fostering the deep integration of AI4SE and SE4AI, establishing a methodology system for intelligent industrial software engineering, and supporting the high-quality development of industrial software.

Submission Guidelines

1. Manuscript Types
(1) Original research papers
(2) High-quality review papers
(3) System and tool papers
(4) Industrial software practice papers
2. Submission Requirements
(1) Submissions must be made via the unified EasyChair portal found on the ChinaSoft 2026 "Call for Papers" webpage. Please ensure you select the track titled "Intelligent Industrial Software Engineering: Large Model-Driven Development, Assurance, and Governance." Formatting guidelines can be found at: http://cjc.ict.ac.cn/wltg/zgjz.htm.
(2) Submissions must represent the authors' own research findings; data must be authentic, reliable, and previously unpublished. Citations must be properly attributed, and authorship must be undisputed. Papers should present a comprehensive account of research with innovative academic insights that contribute positively to the field.
(3) Papers must be well-written and readable, with clear expression and no logical or grammatical errors; the content should be easily accessible and understandable to reviewers.
3. Important Dates
(1) Phase 1: ChinaSoft 2026 Conference Submission
Submission deadline: September 1, 2026 (select this track during submission).
Notification of review results: September 15, 2026.
Deadline for returning revised manuscripts: October 15, 2026 (the track committee will review the revisions).
(2) Phase 2: Conference Presentation and Final Revision Submission
ChinaSoft 2026 Conference: November 2026, Tianjin. Note: For accepted papers, at least one author must present the paper in person at the conference and answer questions from reviewers and the audience; otherwise, the paper will be withdrawn.
Final revision submission deadline: Within one week after the oral presentation at the November conference; revisions should incorporate feedback received from the audience and reviewers during the presentation. (3) Phase 3: Selection for Journal Review
Notification of selection: December 15, 2026. The track's review panel will evaluate submissions and notify the authors of selected conference papers; *Chinese Journal of Computers* will then conduct an internal review to determine whether to publish them.

Topic list

• 1. Development: Large Model-Driven Industrial Software Development Methods
  • (1) Large model-based methods for industrial software requirements analysis: Focusing on knowledge such as industrial protocols, equipment manuals, control logic, production workflows, interface specifications, and industry standards, research is conducted on requirements extraction, understanding, specification generation, defect detection, consistency checking, and traceability.
  • (2) Intelligent development technologies for industrial software code generation and program understanding: Targeting objects such as industrial control logic, embedded programs, industrial service interfaces, automation scripts, and platform components, research focuses on code generation, completion, summarization, translation, program understanding, API recommendation, and automated repair.
  • (3) Technologies for the architectural design and evolution of complex industrial software systems: Targeting complex software such as industrial control systems, Industrial Internet platforms, Manufacturing Execution Systems (MES), and equipment O&M systems, research covers architectural modeling, module partitioning, interface design, architecture recommendation, technical debt identification, architecture evaluation, and evolution methods.
• 2. Assurance: Large Model-Assisted Industrial Software Quality Assurance Methods
  • (4) Software test generation and verification technologies for high-reliability industrial software: Research focuses on methods such as test case generation, test assertion generation, test script maintenance, regression testing, fuzz testing, simulation testing, digital twin testing, hardware-in-the-loop testing, scenario-based testing, and formal verification.
  • (5) Large model-based methods for industrial software defect detection, localization, and repair: Integrating code, logs, call chains, alarm records, equipment status, execution traces, and failure cases, research addresses defect detection, vulnerability identification, defect localization, root cause analysis, fault diagnosis, patch generation, and repair verification.
  • (6) Intelligent technologies for industrial software reliability analysis and operational assurance: Addressing requirements such as real-time performance, high reliability, security, availability, and maintainability, research focuses on reliability modeling, reliability prediction, anomaly detection, fault prediction, operational monitoring, intelligent O&M, and risk assessment methods.
• 3.Governance: Software engineering governance methods for large model applications
  • (7) Compliance and trustworthiness assessment technologies for large models in industrial software engineering: Researching methods for verifying correctness, checking consistency, detecting hallucinations, ensuring license compliance and intellectual property protection, and conducting model audits and liability tracing regarding large-model-generated code, documentation, test cases, patches, and analysis reports.
  • (8) Software engineering governance technologies for large model version evolution: Researching continuous governance methods for large models in industrial software engineering, addressing issues such as version management, capability drift detection, model degradation analysis, change impact assessment, the evolution of prompts and toolchains, reproducibility of generated results, and lifecycle management.
  • (9) Security governance and risk protection technologies for large models in industrial software engineering: Researching methods for prompt injection protection, data leakage detection, code security review, tool invocation oversight, security validation of generated results, software supply chain security, industrial control security, and end-to-end traceability auditing.

Committee

Guest Editor

• Jin Liu (Wuhan University)
• Junping Wang (Institute of Automation, Chinese Academy of Sciences)
• Xinyu Wang (Zhejiang University)
• Wei Dong (National University of Defense Technology)
• Bo Huang (Shanghai University of Engineering Science)

Site

The meeting will be held in Tianjin.

Contact

For all questions regarding submission, please send an email to huangbosues@sues.edu.cn