An Automatic Semantic Code Repair Service Based on Deep Learning for Programs with Single Error

Semantic code repair refers to automatically fix bugs where the actual program code compiles and executes successfully but fail to generate the output the programmer intends. This problem has not been solved very well so far. In this paper, we present a semantic code repair service using a deep attentional sequence-to-sequence model to predict related information about bugs and generate potential fixes without running the program actually. We evaluate the real performance of the semantic code repair service, and verify the feasibility and effectiveness of the service.