ICST 2026
Mon 18 - Fri 22 May 2026 Daejeon, South Korea

Mutation analysis involves mutations of software artefacts that are then used to evaluate the quality of software verification tools and techniques. It is considered the premier technique for evaluating the fault revealing effectiveness of test suites, test generation techniques, and other testing approaches.

Ideas derived from mutation analysis have also been used to test artefacts at different levels of abstraction, including requirements, formal specifications, models, architectural design notations and even informal descriptions. Recently, mutation has played an important role in software engineering for AI, such as in verifying trained models and behaviours. Furthermore, researchers and practitioners have investigated diverse forms of mutation, such as training or test data mutation, in combination with metamorphic testing to evaluate model performance in machine learning and detecting adversarial examples.

To be the premier forum for practitioners and researchers to discuss recent advances in the area of mutation analysis and propose new research directions, Mutation 2025 will feature keynote and invited talks, and will invite submissions of full and short length research paper, full and short length industry papers, and ‘Hot Off the Press’ presentations.

Important Dates

  • Submission deadline: 13 March 2026 (updated)
  • Notification of acceptance: 03 April 2026
  • Camera-ready: TBC
  • Workshop date: 18 May 2026
Plenary
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09:00 - 10:30
Mutation - Session 1Mutation at Room 106
09:00
5m
Day opening
Mutation 2026 Opening
Mutation
Nargiz Humbatova Università della Svizzera italiana, Jeongju Sohn Kyungpook National University, Gunel Jahangirova King's College London
09:05
55m
Keynote
Foes and Frontiers: 
Flakiness, LLMs, and the Future of Mutation Analysis
Mutation
Phil McMinn University of Sheffield
10:00
30m
Paper
A Multi-Perspective Evaluation of Static Mutant Selection Techniques
Mutation
Magdalene Ashong School of Computer Science and Statistics, Trinity College Dublin, Thomas Laurent Lero@Trinity College Dublin, Anthony Ventresque School of Computer Science and Statistics, Trinity College Dublin & Research Ireland Lero
10:30 - 11:00
Coffee BreakCatering at 1F Lobby
10:30
30m
Coffee break
Break
Catering

11:00 - 12:30
Mutation - Session 2Mutation at Room 106
11:00
30m
Paper
Clustering First-Order Mutants by Behavioural Similarity: A Graph-Based Approach to Higher-Order Mutant Generation
Mutation
Sajjad Hesamipour Khelejan , Thomas Laurent Lero@Trinity College Dublin, Anthony Ventresque School of Computer Science and Statistics, Trinity College Dublin & Research Ireland Lero
11:30
30m
Paper
HOP: A Comprehensive Empirical and Theoretical Analysis of Batching Algorithms for Efficient, Safe, Parallel Mutation Analysis in Rust
Mutation
Zalán Lévai University of Sheffield, Donghwan Shin University of Sheffield, Phil McMinn University of Sheffield
12:00
30m
Paper
Round-Trip Mutation Testing: Translating Code to Natural Language Intent and back
Mutation
Asma Sadjida Hamidi SnT, University of Luxembourg, Cedric Richter University of Luxembourg, Ahmed Khanfir RIADI, ENSI, University of Manouba, Tunisia, Mike Papadakis University of Luxembourg
12:30 - 14:00
12:30
90m
Lunch
Lunch
Catering

14:00 - 15:30
Mutation - Session 3Mutation at Room 106
14:00
30m
Talk
QMutBench: A Dataset of Quantum Circuit Mutants
Mutation
Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University
14:30
30m
Paper
Black-Box Test Generation from State Machine Specifications via Mutation and Model Checking
Mutation
Futa Oda Graduate School of Information Science and Technology, Osaka University, Tatsuhiro Tsuchiya Osaka University
15:00
30m
Paper
HOP: MediumDarwin - LittleDarwin Grows with Performance and Research-oriented Extensions
Mutation
Sajjad Hesamipour Khelejan , Thomas Laurent Lero@Trinity College Dublin, Anthony Ventresque School of Computer Science and Statistics, Trinity College Dublin & Research Ireland Lero
15:30 - 16:00
Coffee BreakCatering at 1F Lobby
15:30
30m
Coffee break
Break
Catering

16:00 - 17:30
Mutation - Session 4Mutation at Room 106
16:00
90m
Panel
Panel Discussion
Mutation
Phil McMinn University of Sheffield, Donghwan Shin University of Sheffield, Jinhan Kim Università della Svizzera italiana, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Gregory Gay Chalmers University of Technology and University of Gothenburg

Call for Papers

Mutation analysis involves mutation of software artefacts that are then used to evaluate the quality of software verification tools and techniques. It is considered the premier technique for evaluating the fault revealing effectiveness of test suites, test generation techniques and other testing approaches. Ideas derived from mutation analysis have also been used to test artefacts at different levels of abstraction, including requirements, formal specifications, models, architectural design notations and even informal descriptions. Recently, mutation has played an important role in software engineering for AI, such as in verifying learned models and behaviours. Furthermore, researchers and practitioners have investigated diverse forms of mutation, such as training data or test data mutation, in combination with metamorphic testing to evaluate model performance in machine learning and detecting adversarial examples. Mutation 2026 aims to be the premier forum for practitioners and researchers to discuss recent advances in the area of mutation analysis and propose new research directions. We invite submissions of both full-length and short-length research papers and especially encourage the submission of industry practice papers.

Topics of Interest

Topics of interest include, but are not limited to, the following:

  • Evaluation of mutation-based test adequacy criteria, and comparative studies with other test adequacy criteria.
  • Formal theoretical analysis of mutation testing.
  • Empirical studies on any aspects of mutation testing.
  • Mutation based generation of program variants.
  • Higher-order mutation testing.
  • Mutation testing tools.
  • Mutation for mobile, internet, and cloud based systems (e.g., addressing QoS, power consumption, stress testing, performance, etc.).
  • Mutation for security and reliability.
  • Novel mutation testing applications, and mutation testing in novel domains.
  • Industrial experience with mutation testing.
  • Mutation for artificial intelligence (e.g., data mutation, model mutation, mutation-based test data generation, etc.)
  • Mutation for Deep Learning (e.g., data mutation, model mutation, mutation-based test data generation, etc.).
  • Use of Large Language Models (LLMs) for mutation analysis (e.g. generation of mutants).
  • Mutation for LLMs.

Types of Submissions

Five types of papers can be submitted to the workshop:

  • Full papers (10 pages): Research, case studies.
  • Short papers (5 pages): Research in progress, tools.
  • Full industrial papers (6 pages): Applications and lessons learned in industry.
  • Short industrial papers (2 pages): Mutation testing in practice reports.
  • Hot Off the Press (1 page abstract): presentation of work recently published in other venues

Each paper must conform to the two columns IEEE conference publication format (please use the letter format template and conference option). The text, figures, tables and appendices should not exceed 10 pages. Two additional pages containing only references are permitted.

Submissions will be evaluated according to the relevance and originality of the work and to their ability to generate discussions between the participants of the workshop. Each submission will be reviewed by three reviewers, and all accepted papers will be published as part of the ICST proceedings (except for Hot Off the Press submissions). Mutation 2026 will employ a double-anonymous review process (except for Hot Off the Press and industry submissions). Authors must make every effort to anonymise their papers to hide their identities throughout the review process.

 Industry papers

Industry papers should be given the keyword “industry” and are not subject to the double anonymity policy.

Hot Off the Press

Hot Off the Press submissions should contain 1) a short summary of the paper’s contribution, 2) an explanation of why those results are particularly interesting for MUTATION attendees, 3) a link to the paper. Their title should start with “HOP:” The original paper should be published no earlier than May 1st 2025.

Important Dates

  • Submission deadline: 13 March 2026 AoE (updated)
  • Notification of acceptance: 27 March 2026
  • Camera-ready: 10 April 2026
  • Workshop date: 18 May 2026

Organization

Nargiz Humbatova, Università della Svizzera italiana, Switzerland

Gunel Jahangirova, King’s College London, United Kingdom

Jeongju Sohn, Kyungpook National University, Republic of Korea

Mutation analysis asks a deceptively simple question: does your test suite notice when something goes wrong? But the answers we get are only as reliable as our tests — and only as meaningful as the level at which we choose to mutate. This talk confronts two territories: the foes that quietly undermine mutation analysis from within, and the frontiers that may fundamentally expand what it can do.

It begins with a foe hiding in plain sight: test flakiness. While flaky tests are well-known as a nuisance in software engineering, our work reveals a subtler and more troubling phenomenon — mutations themselves can be the source of flakiness. When a mutant causes a test to pass and fail non-deterministically the core mechanism of mutation analysis is undermined. I will present findings on the prevalence and consequences of flakiness induced by mutations (FLIMsiness).

The talk then turns to frontiers, where large language models are opening new possibilities for how we think about mutation. Traditional mutation operators work at the syntactic level of code. The talk presents an alternative approach: extracting natural language descriptions of code behaviour and mutating at that semantic level, before translating the mutated description back into code. This higher-level mutation strategy has the potential to produce more meaningful, intent-targeting mutants designed to better reflect faults related to what software is supposed to do, not just how it's supposed to do it.

The talk closes by stepping back to sketch some of the open challenges and opportunities that lie ahead for the field, including harnessing AI not just as a tool for generating mutants but as a partner in understanding what those mutants should mean.

Phil McMinn is a Professor of Software Engineering who specialises in software testing. He primarily works on developing automated techniques to assist software engineers in developing test suites that are effective at finding bugs and are efficient to maintain. While he is well-known in the software testing field for his work in search-based automatic test data generation, his research has tackled a variety of problems including test flakiness, test oracles, and ensuring test quality through mutation analysis.

Questions? Use the Mutation contact form.