Voyage's blog post details their approach to evaluating code embeddings for code retrieval. They emphasize the importance of using realistic evaluation datasets derived from actual user searches and repository structures rather than relying solely on synthetic or curated benchmarks. Their methodology involves creating embeddings for code snippets using different models, then querying those embeddings with real-world search terms. They assess performance using retrieval metrics like Mean Reciprocal Rank (MRR) and recall@k, adapted to handle multiple relevant code blocks per query. The post concludes that evaluating on realistic search data provides more practical insights into embedding model effectiveness for code search and highlights the challenges of creating representative evaluation benchmarks.
Voyage's blog post details their evaluation of various code embedding models for code retrieval tasks. They emphasize the importance of using realistic datasets and evaluation metrics like Mean Reciprocal Rank (MRR) tailored for code search scenarios. Their experiments demonstrate that retrieval performance varies significantly across datasets and model architectures, with specialized models like CodeT5 consistently outperforming general-purpose embedding models. They also found that retrieval effectiveness plateaus as embedding dimensionality increases beyond a certain point, suggesting diminishing returns for larger embeddings. Finally, they introduce a novel evaluation dataset derived from Voyage's internal codebase, aimed at providing a more practical benchmark for code retrieval models in real-world settings.
Hacker News users discussed the methodology of Voyage's code retrieval evaluation, particularly questioning the reliance on HumanEval and MBPP benchmarks. Some argued these benchmarks don't adequately reflect real-world code retrieval scenarios, suggesting alternatives like retrieving code from a large corpus based on natural language queries. The lack of open-sourcing for Voyage's evaluated models and datasets also drew criticism, hindering reproducibility and broader community engagement. There was a brief discussion on the usefulness of keyword search as a strong baseline and the potential benefits of integrating semantic search techniques. Several commenters expressed interest in seeing evaluations based on more realistic use cases, including bug fixing or adding new features within existing codebases.
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https://news.ycombinator.com/item?id=42915944
HN users discussed Voyage's methodology for evaluating code embeddings, expressing skepticism about the reliance on exact match retrieval. Commenters argued that semantic similarity is more important for practical use cases like code search and suggested alternative evaluation metrics like Mean Reciprocal Rank (MRR) to better capture the relevance of top results. Some also pointed out the importance of evaluating on larger, more diverse datasets, and the need to consider the cost of indexing and querying different embedding models. The lack of open-sourcing for the embedding model and evaluation dataset also drew criticism, hindering reproducibility and community contribution. Finally, there was discussion about the limitations of current embedding methods and the potential of retrieval augmented generation (RAG) for code.
The Hacker News post "Evaluating Code Embeddings" (https://news.ycombinator.com/item?id=42915944) discussing the Voyage AI blog post about code retrieval evaluation has a modest number of comments, generating a brief but focused discussion.
Several commenters delve into the practicalities and nuances of evaluating code embeddings. One commenter highlights the importance of distinguishing between functional correctness and semantic similarity when assessing retrieved code. They argue that while embeddings might retrieve syntactically similar code, it doesn't guarantee the retrieved code functions identically or even similarly to the query code. This raises the question of what constitutes a "good" retrieval in real-world scenarios where developers prioritize functional equivalence over mere syntactic resemblance.
Another commenter emphasizes the context-dependent nature of code retrieval. They suggest that the ideal retrieval often depends on the user's intent, which can vary widely. Sometimes, a developer might seek functionally equivalent code, while other times they might be looking for code snippets that achieve a similar outcome through different means. This comment underscores the challenge of developing a universally applicable evaluation metric for code retrieval, as the "correct" retrieval is subjective and depends heavily on the developer's specific needs at that moment.
Expanding on the theme of practical application, a commenter discusses the challenges of using code retrieval in large, complex codebases. They point out that embedding models often struggle with long-range dependencies and nuanced contextual information that is crucial for understanding code within a larger project. This limitation can hinder the effectiveness of code retrieval in real-world software development, where code snippets rarely exist in isolation.
Finally, a commenter offers a different perspective by suggesting that evaluating embeddings based on their ability to cluster code into meaningful groups might be a more useful approach. This approach would shift the focus from retrieving individual code snippets to identifying broader conceptual relationships between different parts of a codebase. This could potentially lead to new tools and workflows that leverage code embeddings for tasks like code exploration, refactoring, and even automated code generation.
While the discussion isn't extensive, it touches on several crucial aspects of code retrieval evaluation, highlighting the complexities and open challenges in this area. The comments emphasize the need for evaluation metrics that go beyond superficial syntactic similarity and consider factors like functional correctness, user intent, and the broader context of the codebase.