Measuring Code Efficiency Optimization Capabilities with ACEOB

As Moore's Law gains diminish, software performance and efficiency become increasingly vital. Optimizing code efficiency is challenging, even for professional programmers. However, related research remains relatively scarce, and rigorously assessing models' abilities to optimize code efficiency is fraught with difficulties. In response to this challenge, we first conduct an in-depth analysis of "code patterns" in the model training dataset, meticulously exploring human-written code. Secondly, we define a task for optimizing code efficiency and introduce the Automatic Code Efficiency Optimization Benchmark (ACEOB), which consists of 95,359 pairs of efficient-inefficient code aimed at assessing code efficiency optimization capabilities. To our knowledge, ACEOB is the first dataset specifically targeting Python code efficiency optimization. To evaluate models' ability in optimizing code efficiency, we propose two new metrics: the Isomorphic Optimal Comparison CodeBLEU (IOCCB) metric and the Normalized Performance Index (NPI) metric, to assess the efficiency of model-generated code. We also evaluate several advanced code models, such as PolyCoder and CodeT5, after fine-tuning them on ACEOB and demonstrate that the efficiency of each model improves after introducing the NPI filter. However, it was observed that even ChatGPT does not perform optimally in code efficiency optimization tasks.