Evaluating Large Language Models' Abilities to Assist Programmers in Real-World Coding Tasks

Concepts de base

Improvements in large language model (LLM) performance on static coding benchmarks lead to increased programmer productivity, particularly in reducing time spent on tasks, but gaps in benchmark versus human performance are not proportional. Human preference metrics like suggestion acceptance rate and code copying do not necessarily align with actual programmer performance.

Résumé

The paper introduces RealHumanEval, a web-based platform to conduct human-centric evaluation of LLMs for programming. The platform supports two forms of LLM assistance: autocomplete-based and chat-based.

The authors conducted a user study with 213 participants to understand the effect of LLM performance and the form of assistance on programmer productivity metrics. Key findings:

1. Improvements in LLM benchmark performance lead to gains in human productivity, particularly in reducing time spent on tasks. This trend holds across both autocomplete and chat interactions.

2. However, the gaps in benchmark versus human performance are not proportional - further gains in benchmark performance do not necessarily translate to equivalent gains in human productivity.

3. Human preference metrics like suggestion acceptance rate and code copying from chat responses are only correlated with programmer perceptions of LLM helpfulness, but not with actual programmer performance.

The results highlight the importance of careful evaluation to understand the nuances in programmer-LLM interactions, and the authors encourage the community to leverage RealHumanEval to evaluate new LLMs.

Stats

Participants spent an average of 400 seconds per task in the No LLM condition.
Compared to No LLM, GPT-3.5 and CodeLlama-34b models reduced the time spent per task by 78 and 64 seconds respectively.
CodeLlama-7b models slightly increased the average time spent on a task by 10 seconds.

Citations

"While a set of small-scale user studies have been conducted to primarily build a qualitative understanding of how programmers use LLM assistance, they are typically restricted to evaluations on one model, one form of LLM support, and a limited set of tasks."
"We find that improving a model's base performance on existing coding benchmarks leads to gains in human productivity, particularly in the time spent completing tasks. These trends were present across both chat and autocomplete interactions, validating the potential "generalizability" of benchmarking efforts to more realistic contexts."
"We also investigated whether human preference metrics, such as the average acceptance rate of suggestions and the likelihood of copying code from chat responses, aligned with productivity metrics. While these preference metrics are readily available in real deployments of LLM systems compared to task completion time and thus can be attractive proxy metrics, we find that they are only correlated with programmer perceptions of LLM helpfulness but not necessarily with actual programmer performance."

Idées clés tirées de

The RealHumanEval

by Hussein Moza... à arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02806.pdf

Questions plus approfondies

How can the RealHumanEval platform be extended to capture a broader range of programming tasks and workflows that are representative of real-world software development?

To extend the RealHumanEval platform to capture a broader range of programming tasks and workflows, several enhancements can be implemented:

Diversification of Task Types: Introduce a wider variety of coding tasks that cover different aspects of software development, such as algorithmic problems, data manipulation tasks, debugging scenarios, and code refactoring challenges. This will provide a more comprehensive evaluation of LLMs across various programming domains.

Integration of Real-world Projects: Incorporate tasks that simulate real-world software development projects, including feature implementation, bug fixing, and integration of multiple components. By emulating actual development scenarios, the platform can better assess the practical utility of LLMs in professional settings.

Support for Collaborative Workflows: Enable collaborative coding sessions where multiple users can work together on a shared codebase. This feature can evaluate how LLMs facilitate teamwork, code reviews, and knowledge sharing among developers.

Dynamic Task Generation: Implement a system that dynamically generates coding tasks based on user input, allowing for personalized challenges tailored to individual skill levels and preferences. This adaptive approach can provide a more engaging and relevant experience for participants.

Incorporation of Industry-specific Tasks: Introduce tasks that reflect industry-specific requirements and coding standards, such as financial modeling, healthcare data processing, or e-commerce platform development. This will ensure that the platform evaluates LLMs in contexts relevant to diverse professional fields.

By incorporating these enhancements, RealHumanEval can offer a more comprehensive and realistic evaluation of LLMs in supporting a wide range of programming tasks and workflows.

How can the insights from this study be leveraged to design more effective and personalized LLM-based programming assistants that optimize for both user preference and actual productivity gains?

The insights from this study can be leveraged to design more effective and personalized LLM-based programming assistants through the following strategies:

Context-aware Assistance: Develop LLM models that can adapt their suggestions based on the context of the coding task and the user's programming style. By considering the specific needs and preferences of individual users, the assistants can provide more relevant and tailored support.

Interactive Feedback Mechanisms: Implement interactive feedback mechanisms that allow users to provide real-time input on the quality and relevance of LLM suggestions. This feedback loop can help refine the assistance provided and improve the overall user experience.

Task-specific Customization: Customize the LLM-based assistants to excel in specific types of programming tasks by fine-tuning the models on task-specific datasets and optimizing them for performance in those domains.

Multi-modal Interaction: Integrate multiple modes of interaction, such as chat-based dialogue and autocomplete suggestions, to offer a seamless and intuitive user experience. By combining different interaction methods, the assistants can cater to diverse user preferences and working styles.

Continuous Learning and Improvement: Implement mechanisms for continuous learning and improvement of the LLM models based on user interactions and feedback. By iteratively refining the models, the assistants can evolve to better meet the needs of users over time.

By incorporating these strategies, LLM-based programming assistants can be designed to optimize for both user preference and actual productivity gains, providing tailored and effective support to programmers in their coding tasks.