We give an introduction to reflective towers of interpreters, a semantic model of reflection with a user level interpreted by a meta level interpreted by a meta meta level and so on.
This post discusses the task of automatic code completion through the application of deep learning methods.
The wealth of code now available on-line is fertile ground to enable machine learning to be applied to programming tasks. This post is the second in a series on this topic, focusing on the tasks of semantically labeling and captioning code.
Will machine learning automate programming out of existence, as it is doing for many other professions?
We accept that data structure determines program structure. But we should not forget that it is not just the input data that may be structured: output data may be structured too, and both may determine program structure.