FSE 2026
Sun 5 - Thu 9 July 2026 Montreal, Canada
Tue 7 Jul 2026 17:10 - 17:30 at MB 2.210 - LLM for SE 3 Chair(s): Abbas Heydarnoori

Coding standards are essential for maintaining consistent and high-quality code across teams and projects. Linters help developers enforce these standards by detecting code violations. However, manual linter configuration is complex and expertise-intensive, and the diversity and evolution of programming languages, coding standards, and linters lead to repetitive and maintenance-intensive configuration work. To reduce manual effort, we propose a domain-specific language (DSL)-driven, LLM-based compilation approach to automate configuration generation for coding standards, independent of programming languages, coding standards, and linters. Inspired by compiler design, we first design a DSL to express coding rules in a tool-agnostic, structured, readable, and precise manner. Then, we build linter configurations into DSL configuration instructions. For a given natural language coding standard, the compilation process parses it into DSL coding standards, matches them with the DSL configuration instructions to set configuration names, option names and values, verifies consistency between the standards and configurations, and finally generates linter-specific configurations. Experiments with Checkstyle for Java coding standard show that our approach achieves over 90% precision and recall in DSL representation, with accuracy, precision, recall, and F1-scores close to 70% (with some exceeding 70%) in fine-grained linter configuration generation. Notably, our approach outperforms baselines by over 100% in precision. An ablation study confirms the effectiveness of the main components of our approach. A user study further shows that our approach improves developers’ efficiency in configuring linters for coding standards. Finally, we demonstrate the generality of the approach by generating ESLint configurations for JavaScript coding standards, showcasing its broad applicability across other programming languages, coding standards, and linters. We developed a lightweight, general-purpose AI skill, which is publicly available on GitHub: LintConfig.

Tue 7 Jul

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
16:00
20m
Talk
LACY: Simulating Expert Mentoring for Software Onboarding with Code Tours
Industry Papers
Zeynep Begüm Kara Bilkent University, Aytekin İsmail Bilkent University, Ece Ates Bilkent University, İzgi Nur Tamcı Bilkent University, Zehra İyigün Bilkent University, Selin Şirin Aslangül Beko, Ömercan Devran Beko, Baykal Mehmet Ucar Beko, Eray Tüzün Bilkent University
16:20
20m
Talk
Towards Refining Developer Questions using LLM-Based Named Entity Recognition for Developer Chatroom Conversations
Journal-First Paper
Pouya Fathollahzadeh Queen’s University, Mariam El Mezouar Royal Military College, Hao Li Queen's University, Ying Zou Queen's University, Kingston, Ontario, Ahmed E. Hassan Queen’s University
16:40
20m
Talk
Understanding and Predicting Accepted Code Suggestions in AI-Assisted Programming
Research Papers
Jing Jiang Beihang University, Liehao Li Beihang University, Jinyun Hou Beihang University, Xin Tan Beihang University, Li Zhang Beihang University
Pre-print
17:00
10m
Talk
Context-Aware Feedback Compression in Online Judge Programming with LLMs
Ideas, Visions and Reflections
Jialiang Gu George Mason University, Keren Zhou George Mason University, Daming Li Independent Researcher, Hanyuan Shi N/A, Jialu Zhang University of Waterloo
17:10
20m
Talk
Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
Research Papers
zejun zhang Nanjing University, Yixin Gan Nanjing University, Zhenchang Xing CSIRO's Data61, Tian Zhang Nanjing University, Yi Li Nanyang Technological University, Qinghua Lu Data61, CSIRO, Xiwei (Sherry) Xu Data61, CSIRO, Liming Zhu CSIRO’s Data61
Pre-print