Source-Code Generation Using Deep Learning: A Survey

In recent years, the need for writing effective, reusable, and high-quality source code has grown exponentially. Writing source code is an integral part of building any software system; the development phase of the software life-cycle contains code implementation, refactoring, maintenance, and fixing bugs. Software developers implement the desired solution by turning the system requirements into viable software products. For the most part, the implementation phase can be challenging as it requires a certain level of problem-solving skills and the ability to produce high-quality outcomes without decreasing productivity rates or not meeting the business plans and deadlines. Programmers' daily tasks might also include writing large amounts of repetitive boilerplate code, which can be tedious, not to mention the potential bugs that could arise from human errors during the development process. The ability to automatically generate source code will save significant time and effort invested in the software development process by increasing the speed and efficiency of software development teams. In this survey, we review and summarize the recent studies on deep learning approaches used to generate source code in different programming languages such as Java, Python, and SQL (Structured Query Language). We categorize the surveyed work into two groups, Natural Language-based solutions for approaches that use natural text as input and Computer Vision-based solutions which generate code based on images as input.