Using machine learning and AI to improve your coding is an important area of research. Together with colleagues we work with these techniques, to take them from open source to more industry quality.
There are two great tools that one can use today already. One of the tools is a beta version of add-in for visual studio, which helps software engineers to write code.
https://www.microsoft.com/en-us/ai/ai-lab-code-defect
Microsoft is very active in this area and even has release a set of tools that support the development of AI systems: https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/
Also:
What is great is that the tools are, naturally, available freely!
Another tool is a DeepCode, which analyzes software code and provides suggestions to improve it – e.g. use a specific design pattern or refactoring.
This is great that we have increasingly more tools and that AI engineering matures. We do not want to have precision and recall steer our development. We want to have real testing and real systems. We also need to work with data quality in order to ensure that the systems are reliable.
The alternative is that we use MCC, precision, recall, F1-score to tell us how good a system is, which is not entirely true. These measures do not provide any view on how the system reflects the requirements put on it. These measures allow us to compare different classifiers, but not systems.
I hope that we can focus more discussion on AI quality and not classification quality/accuracy.
