Improving AI-Assisted Programming by Selectively Displaying Code Suggestions Based on Human Feedback

Core Concepts

A utility-theoretic framework to selectively display code suggestions from AI-powered code recommendation systems, leveraging human feedback to improve programmer productivity.

Abstract

This paper proposes a method called Conditional Suggestion Display from Human Feedback (CDHF) to decide when to display code suggestions from AI-powered code recommendation systems, such as GitHub Copilot, to improve programmer productivity. The key insights are: The utility of displaying a code suggestion depends on the likelihood of the programmer accepting the suggestion and the latency in generating the suggestion. The programmer's latent state (e.g., whether they are actively verifying the suggestion or engaged in other tasks) is an important factor in predicting acceptance, but is not captured in typical telemetry data. CDHF employs a two-stage approach to first decide whether to generate a suggestion, and then whether to display it, based on predictive models of acceptance probability. This allows avoiding the generation of suggestions that are likely to be rejected. Experiments on a large dataset of Copilot usage show that CDHF can hide 25% of suggestions that would have been rejected, while increasing the overall acceptance rate by 7.2%. The authors also discuss the potential pitfall of using suggestion acceptance as a reward signal, as it can lead to prioritizing shorter, lower-quality suggestions. The paper provides a principled framework and practical techniques for improving the integration of AI-powered code recommendation systems into the programmer's workflow.

Stats

The dataset contains 4,749 coding sessions from 535 programmers, with a total of 168,807 shown events and 33,523 accepted events, yielding an acceptance rate of 21.4%.

Quotes

"We show that we can hide 25% of suggestions that were shown while guaranteeing that 95% of them would have been rejected." "Retrospectively, if we had used CDHF to hide 52.9% of suggestions, we could compute a counterfactual acceptance rate of 45.6%, which is a 23.1 point increase, a value we expect to be an overestimate."

Key Insights Distilled From

When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

by Hussein Moza... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2306.04930.pdf

Deeper Inquiries

How can the CDHF framework be extended to other types of AI-assisted tasks beyond code recommendation, such as document writing or data analysis?

The CDHF framework, which focuses on deciding when to display code suggestions based on human feedback, can be extended to various AI-assisted tasks beyond code recommendation. For tasks like document writing or data analysis, the framework can be adapted by considering the specific context and requirements of those tasks. Here are some ways in which the CDHF framework can be extended: Task-specific Features: For document writing, features such as document length, writing style, and language complexity can be incorporated into the model to predict the acceptance of text suggestions. Similarly, in data analysis tasks, features related to the dataset, analysis goals, and statistical methods can be used to guide the display of AI-generated suggestions. User Interaction Patterns: Understanding how users interact with AI suggestions in different tasks is crucial. By analyzing user behavior and feedback, the framework can be tailored to optimize the timing and relevance of suggestions in document writing or data analysis workflows. Feedback Mechanisms: Implementing feedback mechanisms similar to those used in code recommendation systems can help improve the quality of AI suggestions in document writing and data analysis. Users can provide explicit feedback on the relevance and accuracy of suggestions, which can be used to refine the recommendation process. Personalization: Just as in code recommendation, personalizing the suggestion display based on individual user preferences and past interactions can enhance the effectiveness of the AI assistance in document writing and data analysis tasks. This can involve learning from user feedback to adapt the suggestions to each user's specific needs and preferences. Integration with Existing Tools: Integrating the CDHF framework with existing document writing or data analysis tools can streamline the workflow and provide real-time assistance to users. By embedding the AI assistance directly into the user interface, the framework can enhance productivity and decision-making in these tasks.

How can the latent state of the user be better captured and incorporated into the decision-making process for displaying suggestions, beyond the telemetry data used in this study?

Capturing and incorporating the latent state of the user, which represents the unobserved activities and thought processes between interactions, is crucial for improving the decision-making process in displaying suggestions. Beyond the telemetry data used in the study, here are some strategies to better capture and incorporate the user's latent state: User Profiling: Develop user profiles based on historical interactions, preferences, and behavior patterns. By analyzing past actions and feedback, the system can infer the user's current state and tailor suggestions accordingly. Contextual Cues: Incorporate contextual cues such as time of day, task complexity, and user workload to infer the user's current focus and engagement level. These cues can provide valuable insights into when to display suggestions effectively. Natural Language Processing: Utilize natural language processing techniques to analyze text input, comments, or notes made by the user during the task. By extracting semantic information and sentiment from the user's text, the system can better understand the user's mindset and adjust suggestion display accordingly. Eye Tracking and Biometric Data: Implement eye-tracking technology and biometric sensors to capture physiological responses and visual attention patterns. These data sources can offer real-time insights into the user's cognitive load, interest, and decision-making processes, enhancing the system's ability to adapt suggestions to the user's mental state. Machine Learning Models: Train machine learning models to predict the user's latent state based on a combination of observable features and historical data. By leveraging advanced algorithms, such as recurrent neural networks or attention mechanisms, the system can learn complex patterns in user behavior and make more informed decisions about when to display suggestions. By integrating these strategies and technologies, the decision-making process for displaying suggestions can be enhanced by capturing and incorporating the user's latent state more effectively, leading to a more personalized and adaptive AI-assisted experience.