YABLoCo: Yet Another Benchmark for Long Context Code Generation

Large Language Models demonstrate the ability to solve various programming tasks, including code generation. Typically, the performance of LLMs is measured on benchmarks with small or medium-sized context windows of thousands of lines of code. At the same time, in real-world software projects, repositories can span up to millions of LoC. This paper closes this gap by contributing to the long context code generation benchmark (YABLoCo). The benchmark featured a test set of 215 functions selected from four large repositories with thousands of functions. The dataset contained metadata of functions, contexts of the functions with different levels of dependencies, docstrings, functions bodies, and call graphs for each repository. This paper presents three key aspects of the contribution. First, the benchmark aims at function body generation in large repositories in C and C++, two languages not covered by previous benchmarks. Second, the benchmark contains large repositories from 200K to 2,000K LoC. Third, we contribute a scalable evaluation pipeline for efficient computing of the target metrics and a tool for visual analysis of generated code. Overall, these three aspects allow for evaluating code generation in large repositories in C and C++.

Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks

Deep neural networks (DNNs) have gained prominence in various applications, such as classification, recognition, and prediction, prompting increased scrutiny of their properties. A fundamental attribute of traditional DNNs is their vulnerability to modifications in input data, which has resulted in the investigation of adversarial attacks. These attacks manipulate the data in order to mislead a DNN. This study aims to challenge the efficacy and generalization of contemporary defense mechanisms against adversarial attacks. Specifically, we explore the hypothesis proposed by Ilyas et. al, which posits that DNN image features can be either robust or non-robust, with adversarial attacks targeting the latter. This hypothesis suggests that training a DNN on a dataset consisting solely of robust features should produce a model resistant to adversarial attacks. However, our experiments demonstrate that this is not universally true. To gain further insights into our findings, we analyze the impact of adversarial attack norms on DNN representations, focusing on samples subjected to $L_2$ and $L_{\infty}$ norm attacks. Further, we employ canonical correlation analysis, visualize the representations, and calculate the mean distance between these representations and various DNN decision boundaries. Our results reveal a significant difference between $L_2$ and $L_{\infty}$ norms, which could provide insights into the potential dangers posed by $L_{\infty}$ norm attacks, previously underestimated by the research community.