A Declarative Framework for Hand-Crafted Mutation Analysis and Management
- URL: http://arxiv.org/abs/2603.07065v1
- Date: Sat, 07 Mar 2026 06:46:16 GMT
- Title: A Declarative Framework for Hand-Crafted Mutation Analysis and Management
- Abstract summary: We present a declarative framework for hand-crafted mutation analysis and management.<n>We describe five mutation representations: comment-based, preprocessor-based, patch-based, match-and-replace, and in-AST mutations.<n>We implement these ideas in Marauder, a prototype system for injecting, activating, resetting, and composing hand-crafted mutants.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hand-crafted mutants are increasingly used to evaluate fuzzing and property-based testing tools, but current tooling is fragmented and often forces trade-offs between readability, mutation preservation, and execution cost. We present a declarative framework for hand-crafted mutation analysis and management. First, we characterize five mutation representations: comment-based, preprocessor-based, patch-based, match-and-replace, and in-AST mutations. Second, we define a mutation algebra that supports selective execution, tag-based expansion, and higher-order combinations of mutants. Third, we describe a lossless conversion pipeline that maps mutation representations through a common intermediate form, including a strategy for extracting and normalizing in-AST mutations. We implement these ideas in Marauder, a prototype system for injecting, activating, resetting, and composing hand-crafted mutants across representations. This framework clarifies the design space of hand-crafted mutation systems and provides a practical foundation for more efficient and expressive mutation experiments.
Related papers
- Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection [58.25418970608328]
We study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to multiple unseen domains.<n>Existing methods mainly rely on simulation-driven strategies, such as data augmentation or textual prompts, to enlarge the training distribution.<n>We propose Manifold Regression with Visual-Text Dual Chain-of-Thought (MR-DCoT), which formulates unknown-domain generalization as a manifold regression problem.
arXiv Detail & Related papers (2026-07-08T09:27:15Z) - Continuous Program Search [4.198653054660764]
We frame this as an operator-design problem: learn a continuous program space where latent distance has behavioral meaning, then design mutation operators that exploit this structure without changing the evolutionary algorithm.<n>We make locality measurable by tracking action-level divergence under controlled latent perturbations, identifying an empirical trust region for behavior-local continuous variation.<n>Although isotropic mutation occasionally attains higher peak performance, geometry-compiled mutation yields faster, more reliable progress, demonstrating that semantically aligned mutation can substantially improve search efficiency without modifying the underlying evolutionary algorithm.
arXiv Detail & Related papers (2026-02-07T18:41:14Z) - A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer [53.85265022754878]
We propose a lightweight DDG predictor (Light-DDG) for fast mutation screening.<n>We also release a large-scale dataset containing millions of mutation data for pre-training Light-DDG.<n>For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences.
arXiv Detail & Related papers (2025-02-10T09:26:57Z) - Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning [78.38442423223832]
We develop a novel codebook pre-training task, namely masked microenvironment modeling.
We demonstrate superior performance and training efficiency over state-of-the-art pre-training-based methods in mutation effect prediction.
arXiv Detail & Related papers (2024-05-16T03:53:21Z) - MMT: Mutation Testing of Java Bytecode with Model Transformation -- An Illustrative Demonstration [0.11470070927586014]
mutation testing is an approach to check the robustness of test suites.
We propose a model-driven approach where mutations of Java bytecode can be flexibly defined by model transformation.
The corresponding tool called MMT has been extended with advanced mutation operators for modifying object-oriented structures.
arXiv Detail & Related papers (2024-04-22T11:33:21Z) - An Empirical Evaluation of Manually Created Equivalent Mutants [54.02049952279685]
Less than 10 % of manually created mutants are equivalent.
Surprisingly, our findings indicate that a significant portion of developers struggle to accurately identify equivalent mutants.
arXiv Detail & Related papers (2024-04-14T13:04:10Z) - Mutation Analysis with Execution Taints [2.574469668220994]
evaluating each mutant separately means a large amount of redundant computation.
We propose execution taints--A novel technique that repurposes dynamic data-flow taints for mutation analysis.
arXiv Detail & Related papers (2024-03-02T09:20:46Z) - MuRS: Mutant Ranking and Suppression using Identifier Templates [4.9205581820379765]
Google's mutation testing service integrates diff-based mutation testing into the code review process.
Google's mutation testing service implements a number of suppression rules, which target not-useful mutants.
This paper proposes and evaluates MuRS, an automated approach that groups mutants by patterns in the source code under test.
arXiv Detail & Related papers (2023-06-15T13:43:52Z) - Self-Supervised Learning for Group Equivariant Neural Networks [75.62232699377877]
Group equivariant neural networks are the models whose structure is restricted to commute with the transformations on the input.
We propose two concepts for self-supervised tasks: equivariant pretext labels and invariant contrastive loss.
Experiments on standard image recognition benchmarks demonstrate that the equivariant neural networks exploit the proposed self-supervised tasks.
arXiv Detail & Related papers (2023-03-08T08:11:26Z) - Equivariant Disentangled Transformation for Domain Generalization under
Combination Shift [91.38796390449504]
Combinations of domains and labels are not observed during training but appear in the test environment.
We provide a unique formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.
arXiv Detail & Related papers (2022-08-03T12:31:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.