HOP: A Comprehensive Empirical and Theoretical Analysis of Batching Algorithms for Efficient, Safe, Parallel Mutation Analysis in Rust
This abstract describes our recently published ACM TOSEM journal article, titled “A Comprehensive Empirical and Theoretical Analysis of Batching Algorithms for Efficient, Safe, Parallel Mutation Analysis in Rust”, published in January 2026. Our article presents our novel technique for batching mutations for parallel, simultaneous evaluation of multiple mutations and their corresponding test cases. Besides our batching technique, our article introduces new mutation operators, distinct safe and unsafe mutations, and a flexible, efficient approach to mutating Rust programs. Our article presents extensive theoretical analysis, and numerous findings on mutation analysis and Rust programs from a large-scale empirical evaluation. Our article is publicly available online under Open Access at https://dl.acm.org/doi/10.1145/3787851.
Mon 18 MayDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 30mPaper | 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 30mPaper | 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 30mPaper | 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 | ||