Recent submissions

Comment · 1 year ago
Unlike most SWE bench submissions, Qodo Command one uses the product directly.

I think that the next step is getting an official "checked" mark by the SWE bench team

Comment · 1 year ago
AI code review will very probably have signal-to-noise problems. It is good to see practical solutions aimed at addressing this. I wonder if fine-tuning models would help - this isn't addressed in the blog.
Comment · 1 year ago
So protecting models behind API isn't working, ha?
Comment · 2 years ago
While Sonnet-3.5 excels in accuracy-token, o1 excels in self-reflection in small isolated tasks. With AlphaCodium, o1 tasks are broken to small isolated tasks, while the flow introduced in AlphaCodium actually guides the steps and overall decision making framework.

We will see more of these frameworks for different use cases

Comment · 2 years ago
Did you get any response from users detecting this automation?
Comment · 2 years ago
Hey, co-creator here, I agree with the sentiment that code coverage may be a proxy and even sometimes a vanity metric but at the same time, IMO unit regression tests are necessary for a maintainable production codebase. I personally don’t feel confident making changes to production code that isn’t tested.

Specifically for generating unit regression tests the Cover-Agent tool already works quite well in the wild for some projects, especially isolated projects (as opposed to complex enterprise-level code). You can see in the few (somewhat cherry-picked) examples we posted [0] that it generates working tests that increase coverage (they were cherry-picked in the sense that these are examples we like to work with often internally at CodiumAI).

I believe that it’s possible to generate additional meaningful tests including end-to-end tests by creating a more sophisticated flow that uses prompting techniques like reflection on the code and existing tests, and generates the tests iteratively, feeding errors and failures back to the LLM to let it fix them. Just as an example. This is somewhat similar to the approach we used with AlphaCodium [1] which hit 54% on the CodeContests benchmark (DeepMind’s AlphaCode 2 hit 43% [2] with the equivalent amount of LLM calls).

If like me you think tests are important but hate writing them, please consider contributing to the open source to help make it work better for more use cases. https://github.com/Codium-ai/cover-agent

[0] https://www.youtube.com/@Codium-AI/videos [1] https://github.com/Codium-ai/AlphaCodium [2] https://storage.googleapis.com/deepmind-media/AlphaCode2/Alp...

Comment · 2 years ago
This is interesting. Are there specific use cases for which it works really well?
Comment · 2 years ago
Comment · 2 years ago
Can it consider an existing code base? rather be inspired by it or even integrate with it?
Comment · 2 years ago
The user can choose the relevant context as follows: go to the relevant code part/snippet/files, select, then Ctrl+shift+E, one by one.

In the near future, the agent will suggest these for you, after first indexing your code base

Comment · 2 years ago
We will support other, I promise
Comment · 2 years ago
SaaS uses best of bread from OpenAI, whole enterprise get to choose also from our own model for on prem deployment
Comment · 2 years ago
Well,

To put it simple: Like copilot, it does have an auto-complete and also chat interface. Different than copilot, it focuses on generating a full code task plan, then having the auto completion work acrroding to your plan, and it check the of quality code.

Like agents, it tries to help you complete a full task, yet,does that in tandem with you, as you work inside tour favorite IDE writing the code with you. In addition, there is focus on code quality, testing, and fetching relevant context from your codebase.

Comment · 2 years ago

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