Metamorphic Debugging for Accountable Software
<|reference_start|>Metamorphic Debugging for Accountable Software: As the laws have become more complicated and enormous, the role of software systems in navigating and understanding these intricacies has become more critical. Given their socio-economic and legally critical implications, ensuring software accountability -- encompassing qualities such as legal compliance, explainability, perceptions of procedural justice, fairness of outcomes, and confidentiality/privacy -- is of paramount social importance. Moreover, software that accurately interprets its requirements, complies with legal standards and upholds social fairness can serve as a surrogate for legal and social norms, enabling policymakers to inquire about the law as seamlessly as a software engineer conducts a test. However, ensuring software accountability faces three key challenges: i) Translating legalese into formal specifications, ii) Lack of a definitive 'truth' for queries (the oracle problem), and iii) Scarcity of trustworthy datasets due to privacy and legal concerns.   Drawing from the experiences in debugging U.S. tax preparation software, we propose that these challenges can be tackled by focusing on relational specifications. While the exact output for a given input may be unknown, the relationship between the outputs of two related inputs may be easier to express. This observation resembles i) the legal doctrine of precedent, meaning that similar cases must yield similar rulings; and ii) metamorphic relation (MR) in software engineering that requires a specific relation between software inputs and outputs. We propose metamorphic debugging as the foundation for detecting, explaining, and repairing socio-legal software for these relations. We showcase recent results that leverage metamorphic debugging to detect and explain accountability bugs in tax prep and poverty management software systems.<|reference_end|>
@article{tizpaz-niari2024metamorphic,
    title={Metamorphic Debugging for Accountable Software},
    author={Saeid Tizpaz-Niari, Shiva Darian, Ashutosh Trivedi},
    journal={arXiv preprint arXiv:2409.16140},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16140},
    primaryClass={cs.SE cs.CY cs.PL}
}