Understanding and Predicting Accepted Code Suggestions in AI-Assisted Programming

AI-assisted programming tools are widely adopted, yet their practical utility is often undermined by undesired suggestions that interrupt developer workflows and cause frustration. While existing research has explored developer-AI interactions when programming qualitatively, a significant gap remains in quantitative analysis of developers’ acceptance of AI-generated code suggestions, partly because the necessary fine-grained interaction data is often proprietary. To bridge this gap, this paper conducts an empirical study using 66,239 industrial developer-AI interactions from a large technology company. We analyze features that are significantly different between accepted code suggestions and rejected ones. We find that accepted suggestions are characterized by significantly higher historical acceptance counts and ratios for both developers and projects, longer generation intervals, shorter preceding code context in the project, and older IDE versions. Based on these findings, we introduce CSAP (Code Suggestion Acceptance Prediction) to predict whether a developer will accept the code suggestion before it is displayed. Our evaluation of CSAP shows that it achieves an accuracy of 0.973 and 0.922 on the imbalanced and balanced datasets, respectively. Compared to a large language model baseline and an in-production industrial filter, CSAP improves the accuracy by 12.6% and 69.5% on the imbalanced dataset, and by 87.0% and 140.1% on the balanced dataset. Our results demonstrate that targeted personalization is a powerful approach for filtering out code suggestions with predicted rejection and reducing developer interruption. To the best of our knowledge, it is the first quantitative study of code suggestion acceptance on large-scale industrial data, and this work also sheds light on an important research direction of AI-assisted programming.