A reproducible, privacy-preserving toolkit and taxonomy that classify human turns and flag cross-cutting consistency work, agent corrections, and retracted requests, and a reproducible, privacy-preserving toolkit and taxonomy that contribute to responsibly engineering EM tooling with AI.
Abstract
Enterprise-modeling (EM) tools are often complex and hard to extend. Yet, users may want to explore new EM features and capabilities that currently do not exist. AI coding agents can help here by enabling the development of new capabilities and entire tools, but whether we can trust a modeling-language tool an LLM largely wrote remains a question. This paper reports on the AI-assisted construction of PM4Py-UCM, an open-source tool that mines Use Case Map (UCM) models from event logs. PM4Py-UCM's capabilities include some expected from process mining tools (e.g., performance heat-maps and dashboards) and distinctive ones (e.g., mined executable scenarios/variants, and model decomposition). We mined the development record itself, composed of 18 agent sessions (374 human turns and 10,328 tool actions over 65 hours), 151 commits, 20 releases, and a test suite grown from 108 to 691 test functions, in order to characterize, in a single in-depth case, how the tool was built with an agent (Claude Code), complemented by an independent static assessment of the resulting code (coverage, complexity, maintainability, security, architecture). We contribute a reproducible, privacy-preserving toolkit and taxonomy that classify human turns and flag cross-cutting consistency work, agent corrections, and retracted requests. Up to version 0.7.4, fixes outnumber features 2.3:1, with ~18% of turns for correcting agent errors. Feature waves dragged a measurable tail of documentation/test/notebook consistency work, and tests grew lockstep with features. We finally present lessons learned, centered on making model transformations mechanically checkable, and the oracle-based validation strategy that closed the"the agent said it works"gap, for responsibly engineering EM tooling with AI.
This work uses event logs extracted from software repositories to discover project-specific agent roles using a predefined SE role vocabulary grounded in repository behavior and generates matching agent specifications and implementations that are aligned with human expectations.
Saimir Bala, Fabiana Fournier, Lior Limonad et al.· 0 citations
Overall, repository-preserved Agent Plans under these tool-specific directories appear to be a narrow but informative artifact for studying task intent and execution guidance in human-agent workflows.
M. Abubakar, Seyedmoein Mohsenimofidi, Jai Lal Lulla et al.· 1 citation
The integration of large language models (LLMs) into software development workflows has given rise to a paradigm known as Agentic Software Engineering (SE 3.0), in which autonomous agents manage full development life cycles under human supervision. This paper presents an exploratory case study in which three LLM-based tools, Antigravity (an agentic IDE with a Gemini 2.5 backend), Gemini CLI, and Qwen Code (local execution via Ollama), are coordinated according to a role-distribution framework proposed in this work as the Role Specialization Model (RSM). Three research questions guide the study: (RQ1) how can LLM-based tools with distinct capabilities be coordinated through the RSM in a real development workflow; (RQ2) what deviations from the planned role distribution emerge during RSM execution and what factors explain them; and (RQ3) how does the resulting product compare against the ISO/IEC 25010 quality model. The objective was to propose the RSM with the incremental development of a Python desktop application for interactive climate-data visualization. The workflow, observed deviations, prompt-hardening techniques, and a qualitative quality assessment are documented. Results suggest that explicit role coordination can support development cycle organization and architectural quality, but requires deliberate coordination strategies, context management, and human verification of agent-generated outputs.
C. Fernández-Y-Fernández, Jorge R. Aguilar-Cisneros· 0 citations
E-Bench is introduced, a fully synthetic benchmark with 323 state-changing tasks across three product domains: Honor of Kings, QQ Music, and Tencent Meeting, and it shows that multi-step tool use remains challenging: Pass^3 stays below 60% for the strongest models, and even with code execution in the E-Bench-Code extension, reliability remains below 70%.
Weihuang Zheng, Tianyuan Zou, Eileen Ye et al.· 1 citation
TestAgent, a multi-agent tool implemented as a VS Code extension that automates the generation of high-quality unit tests for Java projects using repository-level Code Knowledge Graphs, demonstrates its practical utility for regression testing and bug discovery.
Ye Shang, Quanjun Zhang, Zheng Zhan et al.· SIGSOFT FSE Companion· 0 citations
Large language model (LLM) agents are extending electronic design automation (EDA) beyond static RTL generation toward long-horizon, tool-interactive workflows. Yet it remains unclear whether general-purpose coding agents, even with domain-specific EDA skills, can reliably execute an end-to-end RTL-to-GDS flow encompassing synthesis, physical implementation, and engineering change order (ECO) optimization. We evaluate AI agents on a PicoRV32 RTL-to-GDS flow using commercial EDA tools under two timing targets. Their performance is assessed using end-to-end design score, stage completion, and Token ROI, a cost-efficiency metric relating design quality to runtime and cost. Comparing three agent architectures and four foundation models, we derive three practical lessons. First, domain-specific skills improve agents'understanding of individual subtasks but do not ensure reliable completion of a long-horizon EDA flow. Second, agents that achieve similar design progress can still differ by up to 141 times in Token ROI, revealing substantial differences in runtime and cost efficiency. Third, low-level tool-interface mismatches are a major source of physical design failures, particularly when Tcl commands depend on the tool version or execution mode. These results suggest that robust Agentic EDA requires not only stronger models but also structured tool interfaces, persistent design context, controlled execution, and process-level evaluation.
Jinyuan Deng, Zhengrui Chen, Xufeng Wei et al.· 0 citations
