IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis [article]

Hossein Hajipour, Mateusz Malinowski, Mario Fritz
2021 arXiv   pre-print
In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on
more » ... vileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases -- even exceeding prior work that had privileged information on the black-box.
arXiv:2006.10720v2 fatcat:6ey5ta7axbf4lmfwop4xpee4q4