Improving source code suggestion with code embedding and enhanced convolutional long short‐term memory

Abstract

Abstract Source code suggestion is the utmost helpful feature in the integrated development environments that helps to quicken software development by suggesting the next possible source code tokens. The source code contains useful semantic information but is ignored or not utilised to its full potential by existing approaches. To improve the performance of source code suggestion, the authors propose a deep semantic net (DeepSN) that makes use of semantic information of the source code. First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code‐embedding to automatically extract the top‐notch features of the source code and to learn useful semantic information. Next, the source code's long and short‐term context dependencies are captured by using long short‐term memory. We extensively evaluated the proposed approach with three baselines on ten real‐world projects and the results are suggesting that the proposed approach surpasses state‐of‐the‐art approaches. On average, DeepSN achieves 7.6% higher accuracy than the best baseline.

Keywords

• feature extraction
• recurrent neural nets
• semantic networks
• software engineering
• source code (software)
• convolutional neural nets