Search Results

Documents authored by Wu, Jingzheng

Document
Technical Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.33
Blurred Compliance Boundaries: Analyzing Open Source License Conflicts in MCP-Based Agent Workflows

Authors: Xing Cui, Jingzheng Wu, Tianyue Luo, and Xiang Ling

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
Background. Agent systems increasingly invoke heterogeneous third-party services through the Model Context Protocol (MCP), where service licenses, model licenses, and data licenses may be jointly applied during runtime execution. This emerging execution paradigm introduces cross-service compliance conflicts that are difficult to capture with conventional license analysis methods. Existing methods usually assume that the license set under analysis is statically predetermined, making them insufficient for handling novel term semantics in MCP licenses and for analyzing conflict patterns that arise from task-specific selected service subsets. Aims. This paper investigates compositional license conflicts in MCP-based agent workflows. We seek to characterize how license terms interact across compositional MCP services and to provide an automated analysis framework that detects conflicts and identifies license-compatible service replacements. Method. We propose MCP-Licentra, a license conflict analysis framework for MCP-based agent workflows. MCP-Licentra contains four integrated components. First, it extends an existing license term taxonomy with MCP-specific categories and constructs annotated training data for domain adaptation. Second, it fine-tunes License-Llama3-8B with supervised fine-tuning (SFT) and odds ratio preference optimization (ORPO) to recognize license terms and their associated attitudes. Third, it formulates task-specific service selection as a power-set legality verification problem, enabling conflict checking across possible service invocation subsets. Fourth, it recommends license-compatible replacements for conflicting services through semantic embedding retrieval and an iterative compliance checking algorithm under joint functional similarity and license compatibility constraints. Results. We evaluate MCP-Licentra on 3,811 real-world MCP services and 500 compositional service scenarios. MCP-Licentra achieves F1-scores of 88.82% and 86.44% for term identification and license understanding, respectively. For group-level conflict detection, it obtains an FPR of 3.15% and an FNR of 8.77%. For compositional conflict detection, it achieves an overall FPR of 4.90% and an FNR of 3.33%. In addition, MCP-Licentra reaches a resolution success rate of 71.23%, outperforming all baselines. Conclusion. The results show that MCP-based agent workflows introduce compositional compliance risks that are difficult to capture with conventional static license analysis. MCP-Licentra provides an automated framework and empirical evidence for detecting and mitigating such risks in agent systems.
Cite as

Xing Cui, Jingzheng Wu, Tianyue Luo, and Xiang Ling. Blurred Compliance Boundaries: Analyzing Open Source License Conflicts in MCP-Based Agent Workflows. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 33:1-33:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{cui_et_al:LIPIcs.ESEM.2026.33,
  author =	{Cui, Xing and Wu, Jingzheng and Luo, Tianyue and Ling, Xiang},
  title =	{{Blurred Compliance Boundaries: Analyzing Open Source License Conflicts in MCP-Based Agent Workflows}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{33:1--33:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.33},
  URN =		{urn:nbn:de:0030-drops-280017},
  doi =		{10.4230/LIPIcs.ESEM.2026.33},
  annote =	{Keywords: MCP, license conflict detection, compositional license analysis, large language model, agent compliance}
}
Document
Technical Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.43
The Overlooked Spirit of Open Source: LLM-Based Analysis of Non-Explicit Mentoring in OSS Communities

Authors: Xing Cui, Jingzheng Wu, Tianyue Luo, and Xiang Ling

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
Background. The sustainable evolution of Open Source Software (OSS) depends not only on code production but also on continuous knowledge transfer and collaborative support within communities. However, existing contribution metrics primarily center on code commits and merges, overlooking the value of non-coding activities embedded in technical discussions. Among these activities, Non-explicit Mentoring (NEM) refers to guidance-oriented interactions in Pull Request (PR) and Issue discussions, where contributors convey technical knowledge, facilitate problem diagnosis, clarify design rationale, provide normative guidance, and share feedback within natural conversational contexts. Although prior studies acknowledge the existence of such behaviors, systematic quantitative investigations of their scale, structural patterns, and impact remain limited due to reliance on qualitative methods, insufficient automated detection techniques, and the scarcity of high-quality annotated datasets. Aims. To address these challenges, this paper proposes MentoScope, a large language model (LLM)-based automated framework for fine-grained identification and classification of NEM in GitHub collaboration contexts. Method. Built upon Llama-3.1-8B, MentoScope undergoes a three-stage training pipeline. First, continual pre-training (CPT) with LoRA is applied on 92,778 PRs and 118,569 Issues for OSS domain adaptation. Second, supervised fine-tuning (SFT) on 15,214 high-quality annotated samples enables recognition of 8 predefined NEM categories. Third, Odds Ratio Preference Optimization (ORPO) with 3,280 preference pairs aligns model outputs with human judgment to enhance classification robustness. Results. MentoScope achieves F1-scores of 94.20% and 80.13% on NEM identification and classification tasks respectively, significantly outperforming all baselines, with ablation studies confirming the necessity of each training stage. Large-scale analysis of 591,154 comments from 500 OSS projects reveals that NEM is present in over 70% of collaborative interactions, with distributional patterns varying systematically across project scales and discussion contexts. Furthermore, survival analysis on 1,245 newcomer contributors shows that NEM recipients achieve a 12.6% improvement in PR merge rates and a 35% extension in average active tenure compared to the control group. Conclusion. These findings demonstrate that NEM constitutes a prevalent and impactful collaborative behavior in open source communities, with significant implications for newcomer integration and community sustainability.
Cite as

Xing Cui, Jingzheng Wu, Tianyue Luo, and Xiang Ling. The Overlooked Spirit of Open Source: LLM-Based Analysis of Non-Explicit Mentoring in OSS Communities. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 43:1-43:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{cui_et_al:LIPIcs.ESEM.2026.43,
  author =	{Cui, Xing and Wu, Jingzheng and Luo, Tianyue and Ling, Xiang},
  title =	{{The Overlooked Spirit of Open Source: LLM-Based Analysis of Non-Explicit Mentoring in OSS Communities}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{43:1--43:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.43},
  URN =		{urn:nbn:de:0030-drops-280119},
  doi =		{10.4230/LIPIcs.ESEM.2026.43},
  annote =	{Keywords: open source software, non-explicit mentoring, large language models, contributor retention}
}

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact