|
Abstract : |
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 developed a recognition 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. 1, |
