Data scientists have spent decades trying to figure out how to qualitatively evaluate code quality to better understand what is “good” code and what isn’t - objectively, definitively and without emotion. While it is an interesting area to explore, is it a futile effort? After all, it isn’t uncommon for developers to disagree on how code should be approached to reach the same outcome.
Join “Data Science Digest: Source Code Metrics that Matter,” where Dr. Maciej Gryka, Sr. Data Science Manager at Rainforest, shares his compiled learnings on the topics of Data Science, Machine Learning and Software Quality. In this session, he’ll cover:
- Quantitative source code metrics that matter
- How these quality metrics are useful in actual day-to-day work
- How people are trying to solve this problem with machine learning
This is the first in his series of literature reviews for people who are interested in learning about notable research journals.