Abstract Symbolic execution is a popular program analysis technique that has been successfully used for bug-finding and bounded verification in various modern programming languages. Despite its popularity, however, symbolic execution suffers from two main limitations when applied to real-world code: interactions with the runtime environment and path explosion. Symbolic summaries are the standard solution to tackle these challenges. Yet, the development of summaries remains to this day a manual task that is known to be highly error-prone. To address this, we propose SumGen , a new tool for automatically generating correct-by-construction summaries from function specifications. With SumGen , we were able to generate a total of 131 summaries for 47 libc functions, demonstrating the effectiveness of our methodology in producing correct summaries for real-world, highly complex code.

Abstract The issue of fairness is a well-known challenge in Machine Learning (ML) that has gained increased importance with the emergence of Large Language Models (LLMs) and generative AI. Algorithmic bias can manifest during the training of ML models due to the presence of sensitive attributes, such as gender or racial identity. One approach to mitigate bias is to avoid making decisions based on these protected attributes. However, indirect discrimination can still occur if sensitive information is inferred from proxy attributes. To prevent this, there is a growing interest in detecting potential proxy attributes before training ML models. In this case study, we report on the use of Inductive Logic Programming (ILP) to discover proxy attributes in training datasets, with a focus on the ML classification problem. While ILP has established applications in program synthesis and data curation, we demonstrate that it can also advance the state of the art in proxy attribute discovery by removing the need for prior domain knowledge. Our evaluation shows that this approach is effective at detecting potential sources of indirect discrimination, having successfully identified proxy attributes in several well-known datasets used in fairness-awareness studies.