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Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

Anisha Agarwal, Aaron Chan, Shubham Chandel, Jinu Jang, Shaun Miller, Roshanak Zilouchian Moghaddam, Yevhen Mohylevskyy, Neel Sundaresan, Michele Tufano

TL;DR

The Copilot evaluation harness is introduced: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages, and metrics are proposed as a more robust and information-dense evaluation than previous state of the art evaluation systems.

Abstract

The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code Llama offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, utilizing LLMs out of the box is unlikely to be optimal for any given scenario. Rather, each system requires the LLM to be honed to its set of heuristics to ensure the best performance. In this paper, we introduce the Copilot evaluation harness: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages. We propose our metrics as a more robust and information-dense evaluation than previous state of the art evaluation systems. We design and compute both static and execution based success metrics for scenarios encompassing a wide range of developer tasks, including code generation from natural language (generate), documentation generation from code (doc), test case generation (test), bug-fixing (fix), and workspace understanding and query resolution (workspace). These success metrics are designed to evaluate the performance of LLMs within a given IDE and its respective parameter space. Our learnings from evaluating three common LLMs using these metrics can inform the development and validation of future scenarios in LLM guided IDEs.

Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

TL;DR

The Copilot evaluation harness is introduced: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages, and metrics are proposed as a more robust and information-dense evaluation than previous state of the art evaluation systems.

Abstract

The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code Llama offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, utilizing LLMs out of the box is unlikely to be optimal for any given scenario. Rather, each system requires the LLM to be honed to its set of heuristics to ensure the best performance. In this paper, we introduce the Copilot evaluation harness: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages. We propose our metrics as a more robust and information-dense evaluation than previous state of the art evaluation systems. We design and compute both static and execution based success metrics for scenarios encompassing a wide range of developer tasks, including code generation from natural language (generate), documentation generation from code (doc), test case generation (test), bug-fixing (fix), and workspace understanding and query resolution (workspace). These success metrics are designed to evaluate the performance of LLMs within a given IDE and its respective parameter space. Our learnings from evaluating three common LLMs using these metrics can inform the development and validation of future scenarios in LLM guided IDEs.
Paper Structure (42 sections, 13 figures, 3 tables)

This paper contains 42 sections, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs
  4. Evaluating LLMs
  5. Evaluating LLMs for Software Engineering Tasks
  6. Evaluating LLM-Guided Software Programming
  7. Documentation Generation from Code (doc)
  8. Metrics
  9. Evaluation Procedure
  10. Bug-Fixing (fix)
  11. Metrics
  12. Evaluation Procedure
  13. Code Generation from Natural Language (generate)
  14. Metrics
  15. Evaluation Procedure
  16. ...and 27 more sections

Figures (13)

  • Figure 1: A developer has typed the description of a function, which in this case should generate fibonnaci numbers. The LLM has generated the code for this function highlighted in diff format.
  • Figure 2: A developer uses /doc to generate documentation for a function that generates Fibonacci numbers. The LLM generates the documentation for this function highlighted in diff format.
  • Figure 3: A developer asks the model to fix an error in their fibonacci code, and the model presents the fix (spelling the word "yield" correctly) in diff format.
  • Figure 4: A developer asks the model to fix an error in their fibonacci code, and the model presents the fix (spelling the word "yield" correctly) in diff format.
  • Figure 5: A developer uses /test to generate a test for a function that generates Fibonacci numbers. The LLM generates the test_fibonacci function for this function in a test file.
  • ...and 8 more figures