ASET Lab

We develop and evaluate practical software engineering techniques to support the efficient development of robust, maintainable software and cyber-physical systems.

Much of our research is geared towards the growing role of AI in the software-development lifecycle, from agentic software development through to systems such as autonomous vehicles that themselves incorporate AI into their core functionality.

ASET members on their annual walk in the Peak District — ASET members out on their annual walk in the beautiful Peak District

Working with practitioners

Our goal is to develop solutions that address significant Software Engineering and Testing challenges. As such we are always keen to work with industrial partners in the tech community to develop novel solutions that will make an impact. Collaborations can take on a variety of shapes and sizes, from short-term student projects to Ph.D. studentships or more formal collaborative research projects. If you are interested in working with us, please contact us!

News

Nov 03, 2026	We are recruiting for a Lecturer in Software Engineering and AI!
Oct 20, 2026	ASE2026 in Munich!
Oct 07, 2026	The ASET group do their annual walk in the Peaks!

Recent Papers

FlakeFighters: A Pytest Plugin to Detect Flaky Test Failures

Michael Foster, Owain Parry, Michael Hilton, and 2 more authors

In Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering, Oct 2026

DOI Bib

@inproceedings{foster2026flakefighters,
  title = {FlakeFighters: A Pytest Plugin to Detect Flaky Test Failures},
  author = {Foster, Michael and Parry, Owain and Hilton, Michael and Kapfhammer, Gregory M. and McMinn, Phil},
  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering},
  publisher = {ACM},
  pages = {3586--3590},
  year = {2026},
  month = oct,
  doi = {10.1145/3832783.3834641},
}

Marv: An Interactive Tool for Visualising and Reviewing the Results of Mutation Analysis

Daniel Wells, Zalán Lévai, and Phil McMinn

In Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering, Oct 2026

DOI Bib

@inproceedings{wells2026marv,
  title = {Marv: An Interactive Tool for Visualising and Reviewing the Results of Mutation Analysis},
  author = {Wells, Daniel and Lévai, Zalán and McMinn, Phil},
  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering},
  publisher = {ACM},
  pages = {3596--3600},
  year = {2026},
  month = oct,
  doi = {10.1145/3832783.3834643},
}

multiplex: A Modular LLM-Based Mutation Framework

Megan Maton, Gregory M. Kapfhammer, and Phil McMinn

In Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering, Oct 2026

DOI Bib

@inproceedings{maton2026multiplex,
  title = {multiplex: A Modular LLM-Based Mutation Framework},
  author = {Maton, Megan and Kapfhammer, Gregory M. and McMinn, Phil},
  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering},
  publisher = {ACM},
  pages = {3468--3472},
  year = {2026},
  month = oct,
  doi = {10.1145/3832783.3834617},
}

A Comprehensive Empirical and Theoretical Analysis of Batching Algorithms for Efficient, Safe, Parallel Mutation Analysis in Rust

Zalán Lévai, Donghwan Shin, and Phil McMinn

ACM Transactions on Software Engineering and Methodology, Sep 2026

Abs DOI Bib

There is a lack of serious tooling for mutation analysis for Rust, a safety-focused systems programming language seeing increased adoption across the industry. As such, the testing technique has not been widely used on programs written in the language as of yet. Without robust mutation analysis, Rust developers cannot determine test thoroughness. In response to this challenge, we designed a mutation analysis pipeline for Rust, which overcomes the challenges of generating valid mutants caused by the strictness of the language. Our approach accounts for Rust’s distinction between safe and unsafe operations, ensuring that safe mutations of valid Rust programs—those with only valid unsafe code sections—can be safely evaluated within the same process, without the potential for crashes or other undefined behavior invalidating the mutation analysis. We introduce mutation batching, our novel technique for efficiently evaluating multiple mutations simultaneously, while guaranteeing they do not interact. Batching maximizes thread usage, by executing significantly more test cases in parallel. As batching is NP-hard, we present multiple fast approximation algorithms for grouping mutations. We implemented our techniques into a mutation analysis tool, mutest-rs , which we used in our empirical evaluation on a diverse set of 22 Rust libraries and programs. We found that mutation batching reduces the overall runtime of mutation analysis by up to (52.3%) and also saving 73.8 seconds in one case, and that unsafe mutations are detected at a lower rate, of up to (53.4%) . Our mutation analysis tool is available at https://mutest.rs .
@article{levai2026comprehensive, title = {A Comprehensive Empirical and Theoretical Analysis of Batching Algorithms for Efficient, Safe, Parallel Mutation Analysis in Rust}, author = {Lévai, Zalán and Shin, Donghwan and McMinn, Phil}, journal = {ACM Transactions on Software Engineering and Methodology}, volume = {35}, number = {10}, pages = {1--54}, year = {2026}, month = sep, doi = {10.1145/3787851}, }

Causal Software Engineering: A Vision and Roadmap

Roberto Pietrantuono, Luca Giamattei, Stefano Russo, and 2 more authors

In Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering, Jul 2026

DOI Bib

@inproceedings{pietrantuono2026causal,
  title = {Causal Software Engineering: A Vision and Roadmap},
  author = {Pietrantuono, Roberto and Giamattei, Luca and Russo, Stefano and Siebert, Julien and Walkinshaw, Neil},
  booktitle = {Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering},
  publisher = {ACM},
  pages = {1346--1350},
  year = {2026},
  month = jul,
  doi = {10.1145/3803437.3805585},
}