AI Driven Automated Code Review With DeepCode
Summary
Software engineers are frequently faced with problems that have been fixed by other developers in different projects. The challenge is how and when to surface that information in a way that increases their efficiency and avoids wasted effort. DeepCode is an automated code review platform that was built to solve this problem by training a model on a massive array of open sourced code and the history of their bug and security fixes. In this episode their CEO Boris Paskalev explains how the company got started, how they build and maintain the models that provide suggestions for improving your code changes, and how it integrates into your workflow.
Announcements
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- Your host as usual is Tobias Macey and today I’m interviewing Boris Paskalev about DeepCode, an automated code review platform for detecting security vulnerabilities in your projects
Interview
- Introductions
- Can you start by explaining what DeepCode is and the story of how it got started?
- How is the DeepCode platform implemented?
- What are the current languages that you support and what was your guiding principle in selecting them?
- What languages are you targeting next?
- What is involved in maintaining support for languages as they release new versions with new features?
- How do you ensure that the recommendations that you are making are not using languages features that are not available in the runtimes that a given project is using?
- For someone who is using DeepCode, how does it fit into their workflow?
- Can you explain the process that you use for training your models?
- How do you curate and prepare the project sources that you use to power your models?
- How much domain expertise is necessary to identify the faults that you are trying to detect?
- What types of labelling do you perform to ensure that the resulting models are focusing on the proper aspects of the source repositories?
- How do you curate and prepare the project sources that you use to power your models?
- How do you guard against false positives and false negatives in your analysis and recommendations?
- Does the code that you are analyzing and the resulting fixes act as a feedback mechanism for a reinforcement learning system to update your models?
- How do you guard against leaking intellectual property of your scanned code when surfacing recommendations?
- What have been some of the most interesting/unexpected/challenging aspects of building the DeepCode product?
- What do you have planned for the future of the platform and business?
Keep In Touch
Picks
- Tobias
- Redwall Series by Brian Jacques
- Boris
- Artifical Intelligence
- Get outside
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Activity