Using Process Mining to Generate AI Agents from Software Engineering Process Records

• URL: http://arxiv.org/abs/2607.04948v1
• Date: Mon, 06 Jul 2026 11:22:25 GMT
• Title: Using Process Mining to Generate AI Agents from Software Engineering Process Records
• Abstract summary: We address the challenge of integrating AI agents into Software Engineering (SE) teams.<n>Our approach discovers project-specific agent roles using a predefined SE role vocabulary.<n>As proof-of-concept, we applied our approach to a well-established open-source project.
• Score: 1.6974371408199849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating AI agents into Software Engineering (SE) raises an important challenge: how can we specify and realize AI agents that work effectively alongside humans in hybrid SE teams? Determining the right granularity and separation of concerns for such agents is non-trivial. Coarse-grained agents may introduce unmanageable complexity, whereas micro-agents may create severe coordination overhead. Moreover, existing multi-agent SE frameworks typically rely on predefined role structures and do not account for project-specific characteristics or process adaptations. We address this by combining object-centric, imperative, and declarative process mining. Using event logs extracted from software repositories, our approach discovers project-specific agent roles using a predefined SE role vocabulary grounded in repository behavior and generates matching agent specifications and implementations. As proof-of-concept, we applied our approach to a well-established open-source project. We performed functional tests and an exploratory user study to determine how well the generated AI agent specifications are aligned with human expectations.

Related papers

• Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence [115.91847478507736]
  LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks.<n>Many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state.<n>We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole.
  arXiv  Detail & Related papers  (2026-08-21T14:27:57Z)
• From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents [0.0]
  We focus on the operational frameworks that turn AI capabilities into process.<n>Each attack AI development through a different path: spec-driven development in full and lightweight variants, agent-driven agile planning, context engineering over the agent, worktree isolation and review, and recovery of operational specifications from legacy systems.<n>Our central contribution is a six-dimension process taxonomy: specification, context, roles, execution, validation and portability, with a scoring rubric that turns it into a replicable instrument.
  arXiv  Detail & Related papers  (2026-06-03T14:49:15Z)
• AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios [49.90735676070039]
  The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow.<n>We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks.<n>We propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks.
  arXiv  Detail & Related papers  (2026-01-28T13:49:18Z)
• Agentsway -- Software Development Methodology for AI Agents-based Teams [4.226647687395254]
  "Agentsway" is a novel software development framework designed for ecosystems where AI agents operate as first-class collaborators.<n>The framework defines distinct roles for planning, prompting, coding, testing, and fine-tuning agents.<n>Agentsway represents a foundational step toward the next generation of AI-native, self-improving software development methodologies.
  arXiv  Detail & Related papers  (2025-10-26T11:58:42Z)
• Open Agent Specification (Agent Spec): A Unified Representation for AI Agents [10.685555728094338]
  We introduce Open Agent Specification (Agent Spec), a declarative language that defines AI agents and agentic.<n>Agent Spec defines a common set of components, control and data flow semantics, and schemas that allow an agent to be defined once and executed across different runtimes.
  arXiv  Detail & Related papers  (2025-10-05T12:26:42Z)
• Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code [0.0]
  Large Language Models (LLMs) have shown promise in automating code generation and software engineering tasks, yet they often struggle with complex, multi-file projects due to context limitations and knowledge gaps.<n>We propose a novel context engineering workflow that combines multiple AI components: an Intent Translator (GPT-5) for clarifying user requirements, an Elicit-powered semantic literature retrieval for injecting domain knowledge, and a NotebookLM-based document synthesis for contextual understanding, and a Claude Code multi-agent system for code generation and validation.
  arXiv  Detail & Related papers  (2025-08-09T14:45:53Z)
• AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation [0.0]
  We propose a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks.<n>In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code.
  arXiv  Detail & Related papers  (2025-07-26T10:10:02Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Unified Software Engineering agent as AI Software Engineer [14.733475669942276]
  Large Language Model (LLM) technology has raised expectations for automated coding.<n>In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent.<n>We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans.
  arXiv  Detail & Related papers  (2025-06-17T16:19:13Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering [79.07755560048388]
  SWE-agent is a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface (ACI) significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively.
  arXiv  Detail & Related papers  (2024-05-06T17:41:33Z)
• ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
  Large Language Models (LLMs) have emerged human-like intelligence. This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation. We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
  arXiv  Detail & Related papers  (2023-11-02T14:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.