SBIR Phase I:The Development of an Artificial Analysis (AI) Static Code Analysis Platform to Increase Software Developer Productivity

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase the speed and accuracy of software development in a wide range of industries and to make software developers more productive. The technology will decrease the time spent reviewing code by shipping higher quality and defect free code and will further ensure more secure software that is less prone to outside attacks. This SBIR Phase I project develops a cloud-based artificial intelligence (AI)-based static code analysis tool which can find complex and severe problems early in the process of software development. Unlike existing static analysis tools, the tool developed in this project will learn automatically from bug fixes, explain the errors found, and make recommendations on how to fix them.Results will help organizations and developers in the finance, healthcare, and defense industries where code reuse is important for security and compliance reasons. Overall, this project fits well with an increasing trend of organizations integrating more AI into their operations and a growing market for software development tools._x000D_ _x000D_ This SBIR Phase I project combines the latest advancements in machine learning and natural language processing to develop a new, intelligent way to find and explain software errors. The project focuses on developing a software architecture that enables the analysis of a complete model hierarchy, establishing a technique to effectively and quantitatively evaluate the validity of explanations generated for flagged bugs, and integrating the disparate components into a single analysis framework. The project will consist of three models which will be developed and integrated as part of the overarching system architecture: (1) a code fault detection model utilizing a graph attention network, (2) a generative transformer to build explanations and suggestions, and (3) a graph-to-graph transformer to generate mutations to the code architecture to resolve the flagged bugs. The project will leverage recent advancements in transformer-based and graph-based neural networks and therefore propel the current state of research for efficient code review processes forward._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.