CAREER: Enhancing Deep-Learning-based Code Analyses via Human Intelligence

University Of Texas At Dallas, Richardson TX

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). With the increasing availability of the millions of programs in open-source repositories, many techniques have been proposed to leverage deep-learning models to automatically learn patterns from large code bases to assist various software engineering tasks (e.g., security analysis, bug detection). However the proposed deep-learning models still face many input programs that are beyond a model’s handling capability due to many reasons (e.g., evolution of the program code). Because of the lack of understanding about these inputs, many software-engineering applications in industrial practice still make decisions based on symbolic-reasoning systems where decision logic and rules are hard-coded by a human. Human intelligence (e.g., rules summarized by humans) tends to be simplistic and reductionistic, while deep-learning models can be opaque and overfitted. If one can somehow combine the best of the two worlds, many existing challenges will disappear. Therefore, this proposal seeks to make progress on such a combination. The broad goal of this proposal is to design a general framework that improves deep-learning models’ handling of input programs by incorporating human intelligence. Specifically, two main issues are faced by existing deep-learning models in handling code data: (1) lack of understanding about inherent nature of code data, and (2) lack of domain-specific knowledge of software-engineering tasks. To address these fundamental limitations, this project proposes to design a general, user-driven learning-based framework. In the short term, this project aims to improve the practicality of intelligent code-analysis techniques and facilitate the adoption of deep learning techniques in code analysis. In the long run, this project has the potential to fundamentally transform the learning-based techniques for code analysis in software-engineering applications. 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.