DeepCode Analyzes and Cleans your Code with the Help of Machine Learning
- DeepCode is a tool that goes through your code and suggests improvements and finds hidden bugs
- The machine learning model behind the system was trained on a corpus of 250,000 rules
When we write code, we follow certain inherent guidelines since we started programming. There are things we inevitably miss, even when reviewing the script again. This is where machines are proving to be an unprecedented success. Once they are trained to perform a task, they do so with incredible time-saving speed.
DeepCode, a Swiss based organization, has tapped into this space and developed a tool that assists programmers with improving their code, and finding hidden bugs. To perform this, it reads your public and private GitHub repositories. It’s a lot like the super-popular Grammarly tool, but for programmers. It connects to multiple data sources and learns all the information about the code.
As with all machine learning applications, the more data it comes across, the better it gets. It is built to continuously and automatically improve it’s accuracy and precision with each new knowledge it encounters.
The developers behind this system trained their machine learning model on a corpus of almost 250,000 rules. The model has the capability of reading your public and private GitHub repositories and then suggests fixes to the problems in your script. The suggestions are pretty accurate, they make the code remain compatible, and end up improving your program.
We ran the code on a GitHub repository and got the below suggestion to fix the code:
You can try it out on their website.
Our take on this
This is a concept which will be welcomed by data scientists, programmers, and organizations overall. It scans the code and comes up with suggestions really quickly. And the advantage for DeepCode here is that the more people run their code on their tool, the more data they will collect to improve the machine learning model behind this system. And as you can see in the above section, it gave an intuitive suggestion for us!
Are you planning to use this tool? Use the comments below to get involved in the discussion!
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