EAGER: Collaborative Research: Leveraging Graph Databases for Incremental and Scalable Symbolic Analysis and Verification of Web Applications

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Modern human society relies heavily on web applications and is deeply affected by their poor dependability. Unfortunately, no matter how much effort is put into verification and validation of software, existing techniques are inherently limited, and software is routinely released with bugs and issues that limit its functionality and can dramatically affect the user experience. This project investigates an approach that has the potential to significantly improve the scalability and effectiveness of symbolic program analysis and verification techniques, that will improve the dependability of modern web applications,. This project develops techniques and tools that use symbolic execution and automata-based verification techniques to automatically analyze and verify web applications, and store results of symbolic analyses in a graph database for efficient storage, and retrieval. The methods use incremental and differential analysis strategies that utilize the graph database in order to improve scalability and effectiveness of software analysis and verification. The project trains graduate students, and will make its artifacts publicly available to other researchers and educators.