# MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing

arXiv: https://arxiv.org/abs/2408.15815 · HTML: https://mlsanity.com/paper/2408.15815/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [Metamorphic Testing of Large Language Models for Natural Language Processing](https://mlsanity.com/paper/2511.02108/) (2025)
- [CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit](https://mlsanity.com/paper/2411.10842/) (2024)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2510.19438/) (2025)
- [LLM Assisted Coding with Metamorphic Specification Mutation Agent](https://mlsanity.com/paper/2511.18249/) (2025)
- [MR-Coupler: Automated Metamorphic Test Generation via Functional Coupling Analysis](https://mlsanity.com/paper/2604.10126/) (2026)

## Abstract (arXiv)

While a recent study reveals that many developer-written test cases can encode a reusable Metamorphic Relation (MR), over 70% of them directly hard-code the source input and follow-up input in the encoded relation. Such encoded MRs, which do not contain an explicit input transformation to transform the source inputs to corresponding follow-up inputs, cannot be reused with new source inputs to enhance test adequacy. In this paper, we propose MR-Adopt (Automatic Deduction Of inPut Transformation) to automatically deduce the input transformation from the hard-coded source and follow-up inputs, aiming to enable the encoded MRs to be reused with new source inputs. With typically only one pair of source and follow-up inputs available in an MR-encoded test case as the example, we leveraged LLMs to understand the intention of the test case and generate additional examples of source-followup input pairs. This helps to guide the generation of input transformations generalizable to multiple source inputs. Besides, to mitigate the issue that LLMs generate erroneous code, we refine LLM-generated transformations by removing MR- irrelevant code elements with data-flow analysis. Finally, we assess candidate transformations based on encoded output relations and select the best transformation as the result. Evaluation results show that MR-Adopt can generate input transformations applicable to all experimental source inputs for 72.00% of encoded MRs, which is 33.33% more than using vanilla GPT-3.5. By incorporating MR- Adopt-generated input transformations, encoded MR-based test cases can effectively enhance the test adequacy, increasing the line coverage and mutation score by 10.62% and 18.91%, respectively.