Extension-Compression Learning: A deep learning code search method that simulates reading habits

To speed up the efficiency of software development, the ability to retrieve codes through natural language is fundamental. At present, the approach of code search based on deep learning has been extensively researched and achieved a lot of results. However, these models are much complex and the training relies on artificially extracted features. Different from other deep learning models, we simulate people's reading habit of expanding content first and then refining content when learning new knowledge and propose the concept of Extension-Compression Learning. The model can effectively express the features of code and natural language through Extension Learning and Compression Learning. We evaluate the effect of the approach on the code search task with a small dataset and a large dataset, and the results show that all indicators are better than those of other approaches that embed code and text into a joint vector space.