Changshu Liu

Changshu Liu, Yang Chen, Reyhaneh Jabbarvand

This paper proposes CES, a task to evaluate the abilities of LLMs in simulating program execution and using that reasoning in programming tasks. Besides measuring the correctness of variable predictions during execution simulation, CES introduces the notion of coherence to determine whether the simulation complies with commonsense execution logic, even if the predicted values along the simulations are incorrect. This enables CES to rule out suspiciously correct output predictions due to reasoning shortcuts, hallucinations, or potential data leakage. CES also introduces a novel metric to measure reasoning consistency across tests with the same or different prime path coverage in a spectrum: strong, weak, and random. Evaluating 16 LLMs (including three reasoning LLMs) using CES indicates 81.42% coherent execution simulation on HumanEval, 46.92% and 53.08% of which result in correct and incorrect output predictions. Frontier LLMs such as GPT-4 and DeepSeek-R1 have the most incoherent execution reasoning, mostly due to natural language shortcuts. Despite relatively coherent execution simulation, LLMs' reasoning performance across different tests is inconsistent, mostly random (48.87%) or weak (45.37%), potentially explaining their weakness in programming tasks that require path-sensitive program analysis to succeed. We also compare CES with bug prediction/localization/repair, which intuitively requires control- and data-flow awareness. We observe that LLMs barely incorporate execution reasoning into their analysis for bug-related tasks, and their success is primarily due to inherent abilities in pattern matching or natural language shortcuts, if not data leakage. Without reasoning, there is a threat to the generalizability of LLMs in dealing with unseen bugs or patterns in different contexts. CES can be used to vet the suspicious success of LLMs in these tasks systematically.

Changshu Liu, Yang Chen, Reyhaneh Jabbarvand

Large Language Models (LLMs) have been widely used to automate programming tasks. Their capabilities have been evaluated by assessing the quality of generated code through tests or proofs. The extent to which they can reason about code is a critical question revealing important insights about their true capabilities. This paper introduces CodeMind, a framework designed to gauge the code reasoning abilities of LLMs through the following explicit and implicit code reasoning tasks: Independent Execution Reasoning (IER), Specification Reasoning (SR) and Dynamic Semantics Reasoning (DSR). The first evaluates the abilities of LLMs to simulate the execution of given inputs to a code and predict the output (IER). The second assesses the abilities of LLMs to incorporate the simulation of test data in the specification into code generation (SR). Finally, CodeMind evaluates LLMs' abilities to understand overall code semantics only given a specific input/output (DSR). Our extensive evaluation of ten LLMs across four widely used benchmarks using CodeMind shows that LLMs, depending on their size and training strategy, can reason about some dynamic aspects of code. However, their performance drops for code with higher complexity, non-trivial logical and arithmetic operators, non-primitive types, and API calls. We show that these reasoning tasks evaluate LLMs differently, and a comprehensive evaluation of code reasoning requires them all. Finally, we show that the performance of LLMs in bug repair is not correlated with any of the code reasoning tasks, and except for advanced frontier models, other LLMs do not incorporate code reasoning when performing bug repair.