Michael Dorin^{1, 2}, Trang Le², Rajkumar Kolakaluri² and Sergio Montenegro¹, ¹Universität Würzburg, Germany, ²University of St. Thomas, USA

It is commonly understood that code reviews are a cost-effective way of finding faults early in the development cycle. However, many modern software developers are too busy to do them. Skipping code reviews means a loss of opportunity to detect expensive faults prior to software release. Software engineers can be pushed in many directions and reviewing code is very often considered an undesirable task, especially when time is wasted reviewing programs that are not ready. In this study, we wish to ascertain the potential for using machine learning and image recognition to detect immature software source code prior to a review. We show that it is possible to use machine learning to detect software problems visually and allow code reviews to focus on application details. The results are promising and are an indication that further research could be valuable.

Code Reviews, Machine Learning, Imagine Recognition, Coding Style.