Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles (AIware 2026 - Main Track) - AIware 2026

Mon 6 - Tue 7 July 2026 Montreal, Canada

co-located with FSE 2026

Who

Young Jo Chung, Safwat Hassan

Track

AIware 2026 Main Track

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When

Mon 6 Jul 2026 08:50 - 08:55 at MB 1.210 - Coding Agents, Software Testing, and Code Understanding

Abstract

AI coding agents can now open branches, write code, and submit pull requests (PRs). Despite these advancements, we still know little about how agents and humans share responsibilities across the full PR lifecycle. This study analyzes 29,585 PRs from five AI coding tools: OpenAI, Copilot, Devin, Cursor, and Claude Code. Using an Initiator × Approver taxonomy with 6 interaction scenarios, the analysis distinguishes between the PR initiator and the entity with authority to merge it. The observed patterns fall along a Collaborator–Assistant spectrum. In Collaborator tools (Cursor, Devin, Copilot), agents typically initiate PRs that are subsequently reviewed by humans. In contrast, with Assistant tools (OpenAI, Claude), humans initiate most PRs, and a significant proportion are merged without a recorded review step. Across all tools, operational agency and governance authority diverge: agents initiate over 96% of Collaborator PRs, yet less than 0.1% of merges are authorized by agents or automation without explicit human approval. Detailed analysis of all 54 automation‑authorized merges highlights a measurement boundary: event logs indicate who executed the merge, but not who made the decision. This work introduces a lifecycle taxonomy and per‑tool state machines and provides a replication package to support future research on the interplay between automation, oversight, and governance in PR workflows.

Young Jo Chung

Safwat Hassan

University of Toronto

Canada

This program is tentative and subject to change.

Time Zone

The program is currently displayed in (GMT-04:00) Eastern Time (US & Canada).

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The GMT offsets shown reflect the offsets at the moment of the conference.

Time Band

By setting a time band, the program will dim events that are outside this time window. This is useful for (virtual) conferences with a continuous program (with repeated sessions).
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Session Program

Mon 6 Jul
Displayed time zone: Eastern Time (US & Canada) change

	08:50 - 10:30	Coding Agents, Software Testing, and Code UnderstandingArXiv Track / Main Track at MB 1.210

	08:50 5m Talk		Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles Main Track Young Jo Chung , Safwat Hassan University of Toronto
	08:55 5m Talk		When Code Authors Are Agents: A Large-Scale Study of Human–Agent Collaboration in Pull Requests Main Track Anthonia Oluchukwu Njoku École Polytechnique de Montréal, Université de Montréal, CA, Zohreh Sharafi Polytechnique Montréal, Foutse Khomh Polytechnique Montréal
	09:00 5m Talk		Understanding Conversational Patterns in Multi-Agent Programming: A Case Study On Fibonacci Game Development Main Track Srijita Basu Chalmers University of Technology and University of Gothenburg, Viktor Kjellberg Göteborg University, SE, Simin Sun , Bengt Haraldsson Chalmers University of Technology and University of Gothenburg, Scania CV AB, Md Abu Ahammed Babu Volvo Cars AB, Wilhelm Meding Ericsson, Farnaz Fotrousi Chalmers University of Technology and University of Gothenburg, Miroslaw Staron Chalmers University of Technology and University of Gothenburg
	09:05 5m Talk		Recovering from Misbehaviors in Coding Agents Main Track Rahul Nanda Facebook, US, Chandra Maddila Meta Platforms, Inc., Smriti Jha Facebook, US, Euna Mehnaz Khan , Satish Chandra Meta Platforms, Inc., Matteo Paltenghi University of Stuttgart
	09:10 5m Talk		Configuring Agentic AI Coding Tools: An Exploratory Study Main Track Matthias Galster University of Canterbury, Seyedmoein Mohsenimofidi Heidelberg University, Jai Lal Lulla Singapore Management University, Muhammad Auwal Abubakar Otto-Friedrich Universität Bamberg, DE, Christoph Treude Singapore Management University, Sebastian Baltes Heidelberg University Pre-print
	09:15 5m Talk		Execution Control Matters: Deterministic and Agentic Tool Orchestration for LLM-Based Code Translation Main Track Naing Oo Lwin Bucknell University, US, Rajesh Kumar Bucknell University, US
	09:20 5m Talk		Developer Experience with AI Coding Agents: HTTP Behavioral Signatures in Documentation Portals ArXiv Track Oleksii Borysenko Cisco DevNet
	09:25 5m Talk		VISOR: A Vision-Language Model-based Test Oracle for Testing Robots Main Track Prasun Saurabh Simula Research Laboratory, NO, Pablo Valle Mondragon University, Aitor Arrieta Mondragon University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Paolo Arcaini National Institute of Informatics
	09:30 5m Talk		Fixpad++: Automated Bug Fix Verification Using LLM Agents Main Track Mustafa Özkan İr Bilkent University, Bilkent University, TR, Mehmet Dedeler Bilkent University, Bilkent University, TR, Anil Koyuncu Bilkent University, Eray Tüzün Bilkent University
	09:35 5m Talk		Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation Main Track Éric Jacopin Cosmic AI, FR
	09:40 5m Talk		Examining LLMs Ability to Summarize Code Through Mutation-Analysis Main Track Lara Khatib University of Waterloo, Michael Pu University of Waterloo, Bogdan Vasilescu Carnegie Mellon University, Mei Nagappan University of Waterloo
	09:45 5m Talk		Testing AIware Systems: A Software Engineering Survey Main Track Karla Gonzalez Royal Military College of Canada, Mariam El Mezouar Royal Military College
	09:50 5m Talk		TestMap: Evidence Infrastructure for Foundation-Model-Assisted Test Generation ArXiv Track Hunter Leary Virginia Tech, Luke Hanuska Virginia Tech, Chris Brown Virginia Tech
	09:55 5m Talk		Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study ArXiv Track Jie JW Wu Michigan Technological University, USA, Feiyu E Michigan Technological University, USA, Bo Chen Michigan Technological University, USA
	10:00 5m Talk		An Empirical Study of Reasoning Steps in Thinking Code LLMs Main Track Haoran Xue York University, CA, Gias Uddin York University, Canada, Song Wang York University
	10:05 5m Talk		Can LLMs really reason about Code? Studying how well LLMs understand the relation between Input, Code, and Output Main Track Norman Becker CISPA Helmholtz Center for Information Security, DE, Tural Mammadov CISPA Helmholtz Center for Information Security, Andreas Zeller CISPA Helmholtz Center for Information Security
	10:10 5m Talk		How Robustly do LLMs Understand Execution Semantics? Main Track Claudio Spiess University of California, Davis, Premkumar Devanbu UC Davis, Earl T. Barr University College London
	10:15 5m Talk		Program-as-Weights: A Programming Paradigm for Fuzzy Functions ArXiv Track Wentao Zhang University of Waterloo, Liliana Hotsko University of Waterloo, Woojeong Kim Cornell University, Pengyu Nie University of Waterloo, Stuart Shieber Harvard University, Yuntian Deng University of Waterloo
	10:20 10m Live Q&A		Joint Q&A and Discussion Main Track