Retrieved Content about codet

[yiqingxyq]

i am also try to run this project successfully, cheer up and thx for your guys‘ issue, it gives me hope to do it！thx！

If you're interested, here's our implementation of gt retrieval (without filtering the unfinished part): https://github.com/code-rag-bench/code-rag-bench/tree/main?tab=readme-ov-file#retrieval.

[zfj1998]

We are truly sorry the generated content cannot be restored for now. We would love to help you reproduce the results though.

[yiqingxyq]

Thanks for being willing to help! Just want to ask about the retrieval setting. When you do retrieval, do you filter out the file containing the code to complete?

If not, the model is possible to retrieve the target of code generation as the context, which does not make much sense to me -- if you want to use a model to help you complete the code, by the time you call the model, the target code does not exist in the repo yet.

If you do, is there an efficient way to do that?

[zfj1998]

Of course, we need to filter out the target file to avoid leakage. However, we did not filter out all the content in the target file. We keep the content in the front of the target file that is not covered by the context provided to the LM. For example, file A has 100 lines, we have line 20-80 as the unfinished code, and line 81 as the completion hole. During retrieval, we also retrieve line 1-19 as useful supplementary Information for the completion.

The code related to this matter is

CodeT/RepoCoder/search_code.py

Line 44 in 35f54d6

if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:

The context_start_lineno is metadata we stored for each completion case.

[yiqingxyq]

Thanks. The setting makes sense to me!

I retrieved the GT context for the "function" split by adapting your code (window_size=50, slice_size=5). Then I filtered out the unfinished part using your logic (line 44) and run code generation using ChatGPT (2k tokens for GT context, 2k for infile context). I only got Pass@1=0.2895, and you reported 0.4263.

The full evaluation results are:

{
    "EM": 0.10723860589812333,
    "ES": 0.48067297674081083,
    "Pass@1": 0.289544235924933,
}

Here's the GT context I got:
repoeval-function-4k-gt-top5-filter.jsonl.txt

Can you run your code to produce the GT context file for RepoEval-function, so I can compare the difference? Thanks!!!

[binwensun]

i am also try to run this project successfully, cheer up and thx for your guys‘ issue, it gives me hope to do it！thx！

[binwensun]

wow！thx for your support！i will try it again！ 获取 Outlook for iOS<https://aka.ms/o0ukef>

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