Held in conjunction with PACT 2026
October 19, 2026
Chicago, Illinois
ABOUT MLAC
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Using ML tools, such as LLMs and coding assistants are changing the way software is developed. Vibe coding even removes the need for a user to really understand code, but instead requires guiding the model to correcting errors found in the previous generated output and adding new features incrementally. These techniques promise to accelerate software development and/or ease repetitive tasks letting software engineers focus on more complex and important code areas. Easy applications, such as simple, web-based systems, can be fully generated today. More complex applications, such as science simulations using complex physics, specific data mesh designs, intricate data distribution and management, and underlying hardware specific requirements are essentially impossible. This capability range leaves many application classes potentially possible, but with unknown pitfalls for diƯerent kinds of software problems. Working to understand how to generate high quality code with ML tools is essential for productivity today and for the future of the software engineering profession.
This workshop seeks to explore how to use ML-related tools to try to support software development ultimately improving code quality. Human programming introduces errors regularly. The promise of ML-assisted programming is simple errors could be eliminated by using adapted generated code fixed by the ML tools. More subtle errors, such as security vulnerabilities, can use ML tools to do detailed analysis using all available knowledge rather than what any single researcher knows. With the potential success for generated tests, test coverage, code security, and ability to generate code to address complex problems wildly variable, gathering to share recent experiments and developments will help the entire community understand how to better integrate these tools into the software engineering process.
Main topics:
ML-based code generation successes and challenges for various application
domains
ML-based software testing, test coverage, and all other testing related topics
Security auditing using ML tools
Software design assistance using ML tools
Balancing the human and ML costs over the short term through the long term for
software engineering and full product life cycle costs
Related topics of using ML tools to support software related activities
And related topics
To better support Software Engineering professionals, we will accept an abstract for a talk
rather than solely peer reviewed submissions (< 1 page abstract)
Papers will be in the ACM conference format no more than 5 pages long including everything except acknowledgements and references. Submissions are to be double anonymized
Submission Link: https://easychair.org/conferences/?conf=mlac26
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IMPORTANT DATES
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• Submission Deadline: August 14, 2026 AoE
• Responses to Authors: September 4, 2026
• Camera Ready due: October 2, 20226
• Workshop: Monday, 19 October 2026 (morning)
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WORKSHOP ORGANIZATION
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General Chairs:
Jay Lofstead (Sandia National Laboratories)
Proposed Program Committee:
Jim Willenbring (SNL)
Mike Heroux (St. Johns)
Vanessa Sochat (LLNL)
Ewa Deelman (ICI/USC)
Dan Katz (UIUC)
Sandra Gesing (USRSE)
Rosa Filguiera (U of St. Andrews)
Weronika Filinger (EPCC)