Junkai Chen

Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. To evaluate the capabilities of code LLMs in various aspects, many benchmarks have been proposed (e.g., HumanEval and ClassEval). Code reasoning is one of the most essential abilities of code LLMs, but existing benchmarks for code reasoning are not sufficient. Typically, they focus on predicting the input and output of a program, ignoring the evaluation of the intermediate behavior during program execution, as well as the logical consistency (e.g., the model should not give the correct output if the prediction of execution path is wrong) when performing the reasoning. To address these problems, in this paper, we propose a framework, namely REval, for evaluating code reasoning abilities and consistency of code LLMs with program execution. We utilize existing code benchmarks and adapt them to new benchmarks within our framework. A large-scale empirical study is conducted and most LLMs show unsatisfactory performance on both Runtime Behavior Reasoning (i.e., an average accuracy of 44.4%) and Incremental Consistency Evaluation (i.e., an average IC score of 10.3). Evaluation results of current code LLMs reflect the urgent need for the community to strengthen the code reasoning capability of code LLMs.

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language models on code suggestions. We conduct experiments by integrating different language models into our framework and compare the results with their original models. We find that our framework noticeably improves the performance of both code completion and code generation by up to 53.8% and 130.8% in terms of BLEU-4, respectively. Our study highlights that integrating the retrieval process into code suggestions can improve the performance of code suggestions by a large margin.