Round-Trip Mutation Testing: Translating Code to Natural Language Intent and back

• URL: http://arxiv.org/abs/2607.03223v1
• Date: Fri, 03 Jul 2026 11:35:58 GMT
• Title: Round-Trip Mutation Testing: Translating Code to Natural Language Intent and back
• Abstract summary: This paper presents Round-Trip Mutation Testing (RTM)<n>RTM generates mutants from LLM mistranslations between a program code and its intent.<n>Our results demonstrate the potential of round-trip mutation testing to produce syntactically more diverse mutants.
• Score: 5.4804081616439975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents Round-Trip Mutation Testing (RTM), a novel approach that generates mutants from LLM mistranslations between a program code and its intent. Leveraging the generative capability of LLMs from and to programming and natural language, and given an input program, our approach predicts its intent, that is used to generate programs, which when different from the original one, constitute the output mutants. The approach produces additionally mutants, stemming from artificially provoked mistranslations, by mutating the intent prior to the final programs (mutants) generation. Originating from the propagation of small changes in the intent to the code, our intuition is that these programs would present subtle semantic differences from the original one, simulating likely-to-occur faults that could result from specification misunderstandings, and enabling mutation testing. To evaluate RTM, we run it on 40 real buggy methods and evaluate its effectiveness and cost-efficiency in guiding testing towards detecting the bugs. Our results demonstrate the potential of round-trip mutation testing to produce syntactically more diverse mutants, potentially exposing faults that traditional mutation operators fail to reveal. More interestingly, RTM outperforms traditional pattern-based mutation in producing smaller and stronger test-suites, detecting on average over 4 and 1.7 times more faults when selecting only 4 and 30 tests respectively.

Related papers

• Intent-Based Mutation Testing: From Naturally Written Programming Intents to Mutants [5.317864735982288]
  intent-based mutation testing generates mutations by changing the programming intents that are implemented in the programs under test.<n>We implement intent-based mutation testing using Large Language Models (LLMs) that mutate programming intents and transform them into mutants.<n>We evaluate intent-based mutation on 29 programs and show that it generates mutations that are syntactically complex, semantically diverse, and quite different (semantically) from the traditional ones.
  arXiv  Detail & Related papers  (2026-07-06T14:30:02Z)
• Hybrid Fault-Driven Mutation Testing for Python [0.0]
  We introduce a novel set of seven mutation operators inspired by prevalent anti-patterns in Python programs.<n>We propose a mutation testing technique that utilizes a hybrid of static and dynamic analyses.<n>We implement our approach in a tool called PyTation and evaluate it on 13 open-source Python applications.
  arXiv  Detail & Related papers  (2026-01-27T01:49:38Z)
• PRIMG : Efficient LLM-driven Test Generation Using Mutant Prioritization [0.0]
  PRIMG (Prioritization and Refinement Integrated Mutation-driven Generation) is a novel framework for incremental and adaptive test case generation for Solidity smart contracts.<n> PRIMG integrates a mutation prioritization module, which employs a machine learning model trained on mutant subsumption graphs to predict the usefulness of surviving mutants.<n>The prioritization module consistently outperformed random mutant selection, enabling the generation of high-impact tests with reduced computational effort.
  arXiv  Detail & Related papers  (2025-05-08T18:30:22Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions.<n>Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• A Comprehensive Study on Large Language Models for Mutation Testing [36.00296047226433]
  Large Language Models (LLMs) have recently been used to generate mutants in both research work and in industrial practice.<n>We evaluate BugFarm and LLMorpheus (the two state-of-the-art LLM-based approaches) on 851 real bugs from two Java real-world bug benchmarks.<n>Our results reveal that, compared to existing rule-based approaches, LLMs generate more diverse mutants, that are behaviorally closer to real bugs and, most importantly, with 111.29% higher fault detection.
  arXiv  Detail & Related papers  (2024-06-14T08:49:41Z)
• LLMorpheus: Mutation Testing using Large Language Models [5.448283690603358]
  This paper presents a technique for mutation testing where placeholders are introduced at designated locations in a program's source code.<n>We find LLMorpheus to be capable of producing mutants that resemble existing bugs that cannot be produced by StrykerJS.
  arXiv  Detail & Related papers  (2024-04-15T17:25:14Z)
• 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)
• Contextual Predictive Mutation Testing [17.832774161583036]
  We introduce MutationBERT, an approach for predictive mutation testing that simultaneously encodes the source method mutation and test method. Thanks to its higher precision, MutationBERT saves 33% of the time spent by a prior approach on checking/verifying live mutants. We validate our input representation, and aggregation approaches for lifting predictions from the test matrix level to the test suite level, finding similar improvements in performance.
  arXiv  Detail & Related papers  (2023-09-05T17:00:15Z)
• DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text [82.5469544192645]
  We propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT) By analyzing the differences between the original and new remaining parts through N-gram analysis, we unveil significant discrepancies between the distribution of machine-generated text and human-written text. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text.
  arXiv  Detail & Related papers  (2023-05-27T03:58:29Z)
• Confident Adaptive Language Modeling [95.45272377648773]
  CALM is a framework for dynamically allocating different amounts of compute per input and generation timestep. We demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $times 3$ -- while provably maintaining high performance.
  arXiv  Detail & Related papers  (2022-07-14T17:00:19Z)
• Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction [49.25830718574892]
  We present a new framework named Tail-to-Tail (textbfTtT) non-autoregressive sequence prediction. Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected. Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure.
  arXiv  Detail & Related papers  (2021-06-03T05:56:57Z)

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.