Flexible Control for Program Recognition

Recognizing commonly used data structures and algorithms is a key activity in reverse engineering. Systems developed to automate this recognition process have been isolated, stand-alone systems, usually targeting a specific task. We are interested in applying recognition to multiple tasks requiring reverse engineering, such as inspecting, maintaining, and reusing software. This requires a flexible, adaptable recognition architecture, since the tasks vary in the amount and accuracy of knowledge available about the program, the requirements on recognition power, and the resources available. We have developedarecognition system based on graph parsing. It has a flexible, adaptable control structure that can accept advice from external agents. Its flexibility arises from using a chart parsing algorithm. We are studying this graph parsing approach to determine what types of advice can enhance its capabilities, performance, and scalability