Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
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.
65d80fcaab0790921b3141d6
Anisha Agarwal
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2024-02-23T13:07:11.310Z
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Anisha Agarwal
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24anisha
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Aaron Chan
admin_assigned
2024-02-23T13:07:23.655Z
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Aaron_Chan
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AaronChan
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Shubham Chandel
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2024-02-23T13:07:42.487Z
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Shubham Chandel
user
sksq96
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Jinu Jang
admin_assigned
2024-02-23T13:07:49.280Z
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Jinu Jang
user
ethanj-dev
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Shaun Miller
65d80fcaab0790921b3141db
Roshanak Zilouchian Moghaddam
65d80fcaab0790921b3141dc
Yevhen Mohylevskyy
65d80fcaab0790921b3141dd
Neel Sundaresan
65d80fcaab0790921b3141de
Michele Tufano
admin_assigned
2024-02-23T13:08:22.454Z
65cee0e59aa49d5c96409bc7
/avatars/69c8a0b28ab20c83fa30cbd9d427246d.svg
Michele Tufano
user
michele081tufano
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
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.
The Copilot evaluation harness assesses the performance of Large Language Models in IDEs using a variety of programming tasks and metrics, offering a more robust evaluation system.
Large Language Models LLMs OpenAI GPT-3.5/4 Code Llama Copilot evaluation harness code generation documentation generation test case generation bug-fixing workspace understanding query resolution