Thanks John, this is a great workflow, especially the part about having the model summarize what did and did not work, then feeding those summaries back in as context. That "what broke and what fixed it" record is basically the signal my dataset tries to capture, just at the dependency-upgrade level. Drift really is the quiet killer. Appreciate you sharing the prompt link too, I'll take a look.
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upvoted a collection 3 days ago
DepDoctor repliedto their post 3 days ago
Every Python developer has hit this: you upgrade numpy or pandas, and code that worked yesterday breaks today.
I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes.
A few things I tried to get right:
- 935 "leave it alone" examples, to teach a model restraint, not just what to change.
- Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth.
- The main failure mode, over-editing, is measured and explained rather than buried.
Dataset, fine-tuned model, and a live demo are all open in one place:
https://huggingface.co/collections/Abhisek987/depdoctor
Feedback welcome, especially from anyone working on code repair or API migration. repliedto their post 3 days ago
Every Python developer has hit this: you upgrade numpy or pandas, and code that worked yesterday breaks today.
I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes.
A few things I tried to get right:
- 935 "leave it alone" examples, to teach a model restraint, not just what to change.
- Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth.
- The main failure mode, over-editing, is measured and explained rather than buried.
Dataset, fine-tuned model, and a live demo are all open in one place:
https://huggingface.co/collections/Abhisek987/depdoctor
Feedback welcome, especially from anyone working on code repair or API migration.Organizations
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replied to their post 3 days ago
replied to their post 3 days ago
Nice!
Drift is always, always really troublesome. If there were no drift, Python would be heaven. Well, it does have weaknesses like speed, GIL constraints, and indentation, but...
It’s not that I’m seriously working on it as research; it’s just a bit of everyday wisdom, but when I debug Spaces, if they are meant to be open/public, I first feed the code into ChatGPT. Then I deliberately debug it within the chat, whether by vibe coding or not, and at the end I have it summarize what didn’t work and what made it work.
Then, if I feed the generated file into ChatGPT and use it as-is, or feed several of them in together, add context, and have it summarize them into another Markdown document before attaching it, it tends to refer to them fairly well.😅
Nice!
Drift is always, always really troublesome. If there were no drift, Python would be heaven. Well, it does have weaknesses like speed, GIL constraints, and indentation, but...
It’s not that I’m seriously working on it as research; it’s just a bit of everyday wisdom, but when I debug Spaces, if they are meant to be open/public, I first feed the code into ChatGPT. Then I deliberately debug it within the chat, whether by vibe coding or not, and at the end I have it summarize what didn’t work and what made it work.
Then, if I feed the generated file into ChatGPT and use it as-is, or feed several of them in together, add context, and have it summarize them into another Markdown document before attaching it, it tends to refer to them fairly well.😅
replied to their post 9 days ago
Good question. It is for training and evaluating models that fix Python code after a dependency upgrade breaks it. If you have ever bumped numpy or pandas and watched old code stop working, this is the data to teach a model to make those fixes automatically. Each example pairs the broken code with the fix and a short note on the API change that caused it. There is a live demo in the collection if you want to see it fix real code.
posted an update 10 days ago
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Every Python developer has hit this: you upgrade numpy or pandas, and code that worked yesterday breaks today.
I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes.
A few things I tried to get right:
- 935 "leave it alone" examples, to teach a model restraint, not just what to change.
- Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth.
- The main failure mode, over-editing, is measured and explained rather than buried.
Dataset, fine-tuned model, and a live demo are all open in one place:
https://huggingface.co/collections/Abhisek987/depdoctor
Feedback welcome, especially from anyone working on code repair or API migration.
I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes.
A few things I tried to get right:
- 935 "leave it alone" examples, to teach a model restraint, not just what to change.
- Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth.
- The main failure mode, over-editing, is measured and explained rather than buried.
Dataset, fine-tuned model, and a live demo are all open in one place:
https://huggingface.co/collections/Abhisek987/depdoctor
Feedback welcome, especially from anyone working on code repair or API migration.
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