As the scope of software projects increases, it becomes increasingly difficult to ensure proper code quality and conduct efficient code reviews, not because the required tools lack, but because all relevant information tends to become buried under unnecessary noise. In an attempt to combat that issue, CodeFox is presented as a lightweight, modular solution for discovering valuable insights by leveraging modern AI algorithms. In terms of functionality, CodeFox consists of a developer-friendly user interface, various automated review tools, and a meaning- based code search module. The platform collects metadata such as commits, reviews, comments, as well as ownership information and uses semantic embeddings to index critical elements within the code. By leveraging vector search algorithm, CodeFox allows its users to easily discover similar code, discussion threads related to the code, as well as reviewers who were working on this code. The use of the platform at an early stage of development in our company allowed us to shorten the review cycle process as well as reveal the reasoning behind code changes more efficiently. In this paper, we discuss CodeFox architecture, key aspects of integration, namely webhooks, background workers, and persistent storage, as well as share our experience of implementing a convenient yet lightweight platform to facilitate further development. We plan to conduct a user study in order to evaluate efficiency in the context of the problem and implement more advanced data gathering features and intelligent suggestions in the future.
Utsav R. Hirapra (Thu,) studied this question.