Software architecture enables our ability to innovate through extensible design, which provides for future growth in capability that is affordable and timely. To reduce the time needed to field capabilities and to lower lifecycle costs, the DoD has instructed program managers to consider a modular open systems approach (MOSA). MOSA promotes extensibility through technical standards such as the Future Airborne Capability Environment (FACE). However, a gap exists in verifying whether implemented capabilities satisfy the design constraints of a reference architecture such as FACE.

This project is creating an automated conformance checker that can be integrated into the continuous integration workflow to detect and report nonconformances in hours instead of the months or years it takes to discover these problems today. This technology will correctly identify design nonconformances with precision greater than 90%.

The central research of this project is recognizing abstractions commonly used in software architecture in C++ source code. Extracting design from code is hard because there are few indications of intent in the code and because implementations show significant variations. We see potential in applying code analysis, software architecture knowledge, and machine learning to extract design as implemented in the code and check conformance with the intended design. We are focusing on detecting nonconformance with architecture communication styles that are essential to achieving the goals of MOSA.