AIware 2026
Mon 6 - Tue 7 July 2026 Montreal, Canada
co-located with FSE 2026

Artificial intelligence is rapidly changing the landscape of software development. With the unique ability to quickly generate code and the potential to disrupt traditional workflows, AI tools have found growing adoption within the software development process. Subsequently, this topic has been the focus of academic work, including research examining qualitative impacts to productivity and the analysis of sentiments from the developers who utilize AI tools. While this material is extensive, our research team identified a gap within existing literature: what do software managers have to say? The overarching goal of this study is to examine the views of software managers on how AI tools have affected software development. We seek to understand how managers, who leverage a top-down view of the development process, perceive the influence of AI on developers, their own roles, and the broader labor market. To answer these questions, we conducted an empirical study by releasing an online questionnaire containing both qualitative and quantitative questions, sampling software managers employed across both tech-focused and non-tech-focused companies. Through a survey of 42 managers, we found that managers hold nuanced views on the introduction of AI into software development. They encourage developers to use AI, perceive it as valuable for testing, and apply it themselves for knowledge work. At the same time, they raise concerns about privacy, responsibility, transparency, and over-reliance. Many also predict a loss of jobs within the software development market due to consolidation driven by AI. Overall, AI is seen by managers as both a powerful productivity tool and a source of new ethical challenges. Our investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.

Tue 7 Jul

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

14:00 - 15:30
Human Factors, Responsible AIware, and Benchmarks & DatasetsBenchmark & Dataset Track / Main Track at MB 1.210
Chair(s): Diego Elias Costa Concordia University, Canada
14:00
5m
Talk
Is Artificial Intelligence an Elixir to the Software Engineering Community? An Empirical Study among ManagersACM SIGSOFT Distinguished Paper Award
Main Track
Xin Zhao Seattle University, Brian Vu Seattle University, US, Sitesh Pattanaik Donald Bren School of Information and Computer Sciences, University of California, Irvine, US
DOI
14:05
5m
Talk
Towards AI as a Collaborative Partner: A Taxonomy of AI Agent Behavior in Software Engineering
Main Track
Tao Dong Google, Sherry Shi Google, Harini Sampath , Andrew Macvean Google, Inc.
DOI Pre-print
14:10
5m
Talk
Auditing Who Appears to Belong: A Large-Scale Empirical Study of Bias in Deployed Text-to-Image Systems for Software Engineering
Main Track
Mohamad Kassab Boston University
DOI
14:15
5m
Talk
Operationalizing Ethics for AI Agents: How Developers Encode Values into Repository Context Files
Main Track
Christoph Treude Singapore Management University, Sebastian Baltes Heidelberg University, Marc Cheong the University of Melbourne
DOI Pre-print
14:20
5m
Talk
Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
Main Track
Christoph Treude Singapore Management University
DOI Pre-print
14:25
5m
Talk
SOSecure: The Wisdom of the Crowd for Safer AI-Generated Code
Main Track
Manisha Mukherjee Carnegie Mellon University, Vincent J. Hellendoorn Google DeepMind
DOI
14:30
5m
Talk
SecVulEval: Context-Aware Benchmarking of LLMs for Vulnerability DetectionAIware Best Benchmark/Dataset Paper Award
Benchmark & Dataset Track
Md Basim Uddin Ahmed York University, CA, Nima Shiri Harzevili York University, Jiho Shin York University, Hung Viet Pham York University, Song Wang York University
DOI
14:35
5m
Talk
SecMutBench: Evaluating LLM-Generated Security Tests via Mutation-Based Vulnerability Detection
Benchmark & Dataset Track
Mariam ALMutairi Virginia Polytechnic Institute and State University, US
DOI
14:40
5m
Talk
CrossCommitVuln-Bench: A Dataset of Multi-commit Python Vulnerabilities Invisible to Per-Commit Static Analysis
Benchmark & Dataset Track
Arunabh Majumdar Independent Researcher, IN
DOI
14:45
5m
Talk
REBench: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names
Benchmark & Dataset Track
Jun Yeon Won Ohio State University, Columbus, US, Xin Jin Meta, Shiqing Ma University of Massachusetts at Amherst, Zhiqiang Lin The Ohio State University
DOI
14:50
5m
Talk
RustBuildEq: A Benchmark for Binary Equivalence under Build Variability
Benchmark & Dataset Track
Elliott Wen The University of Auckland, Chenye Ni , Valerio Terragni University of Auckland, Jens Dietrich Victoria University of Wellington
DOI
14:55
5m
Talk
TOGBench: A Developer-Written Multi-variant Dataset and Benchmark Suite for Test Oracle Generation
Benchmark & Dataset Track
Tasfia Tasnim University of Texas at Dallas, US, Matthew B Dwyer University of Virginia, Soneya Binta Hossain University of Texas at Dallas
DOI
15:00
5m
Talk
HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
Benchmark & Dataset Track
Fazle Rabbi Concordia University, Jinqiu Yang Concordia University
DOI
15:05
5m
Paper
JunoBench: A Benchmark Dataset of Crashes in Python Machine Learning Jupyter Notebooks
Benchmark & Dataset Track
Yiran Wang Linköping University, José Antonio Hernández López Department of Computer Science and Systems, University of Murcia, Ulf Nilsson Linköping University, Daniel Varro Linköping University / McGill University
DOI Pre-print
15:10
5m
Talk
AgentTelemetry: A Fault Detection Benchmark and Toolkit for LLM Agent Observability
Benchmark & Dataset Track
DOI
15:15
15m
Live Q&A
Joint Q&A
Benchmark & Dataset Track