Metamorphic Testing and Debugging of Tax Preparation Software
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Abstract (arXiv)
This paper presents a data-driven framework to improve the trustworthiness of US tax preparation software systems. Given the legal implications of bugs in such software on its users, ensuring compliance and trustworthiness of tax preparation software is of paramount importance. The key barriers in developing debugging aids for tax preparation systems are the unavailability of explicit specifications and the difficulty of obtaining oracles. We posit that, since the US tax law adheres to the legal doctrine of precedent, the specifications about the outcome of tax preparation software for an individual taxpayer must be viewed in comparison with individuals that are deemed similar. Consequently, these specifications are naturally available as properties on the software requiring similar inputs provide similar outputs. Inspired by the metamorphic testing paradigm, we dub these relations metamorphic relations. In collaboration with legal and tax experts, we explicated metamorphic relations for a set of challenging properties from various US Internal Revenue Services (IRS) publications including Publication 596 (Earned Income Tax Credit), Schedule 8812 (Qualifying Children/Other Dependents), and Form 8863 (Education Credits). We focus on an open-source tax preparation software for our case study and develop a randomized test-case generation strategy to systematically validate the correctness of tax preparation software guided by metamorphic relations. We further aid this test-case generation by visually explaining the behavior of software on suspicious instances using easy to-interpret decision-tree models. Our tool uncovered several accountability bugs with varying severity ranging from non-robust behavior in corner-cases (unreliable behavior when tax returns are close to zero) to missing eligibility conditions in the updated versions of software.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 3.
7 papers.
- 2023Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networkscited by 10
- 2025An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Softwarecited by 5
- 2023On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Softwarecited by 3
- 2024Software Engineering Methods For AI-Driven Deductive Legal Reasoningcited by 3
- 2025Technical Challenges in Maintaining Tax Prep Software with Large Language Modelscited by 3
- 2025Fairness Testing through Extreme Value Theorycited by 2
- 2024Predicting Fairness of ML Software Configurationscited by 1
- 2025An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software16 Sep 2025
- 2025Technical Challenges in Maintaining Tax Prep Software with Large Language Models25 Apr 2025
- 2025Fairness Testing through Extreme Value Theory20 Jan 2025
- 2024Predicting Fairness of ML Software Configurations29 Apr 2024
- 2024Software Engineering Methods For AI-Driven Deductive Legal Reasoning15 Apr 2024
- 2023On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software20 Nov 2023
- 2023Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks9 Apr 2023