4/10/2023 0 Comments Uh manoa logicworks![]() Mackworth, A., Mulder, J., and Havens, W. Algorithms for constraint-satisfaction problems. Automated program understanding by concept recognition. Working Notes of the Third Workshop on AI and Software Engineering: Breaking the Toy Mold (AISE), pp. Detecting code similarity using patterns. Kontogiannis, K., Galler, M., and DeMori, R. Localization of design concepts in legacy systems. ![]() Kontogiannis, K., DeMori, R., Bernstein, R., and Merlo, M. of the Fifth National Conference on Artificial Intelligence, Philadelphia, Pennsylvania, pp. Technical report TR-95-07, University of Ottawa. Speeding up problem-solving by abstraction: A graph-oriented approach. Holte, R., Mkadmi, T., Zimmer, R., and MacDonald, A. Increasing tree-search efficiency for constraint satisfaction problems. A sufficient condition of backtrack-free search. Artificial Intelligence, 41:89-95.įreuder, E.C. Incorporating default inferences into plan recognition. Computational Linguistics, 14(3):23-37.Ĭarberry, S. Another advantage is the improvement in search efficiency using various heuristic techniques in constraint satisfaction.Ĭarberry, S. One advantage of the constraint satisfaction model is its generality many previous attempts in program understanding could now be cast under the same spectrum of heuristics, and thus be readily compared. We then systematically study different search algorithms and empirically evaluate their performance. Within this model we intelligently compose a partial global picture of the source program code by transforming knowledge about the problem domain and the program itself into sets of constraints. In this paper we present a model of program understanding using constraint satisfaction. These mappings could be used to assist an expert in reverse engineering legacy code, to facilitate software reuse, or to assist in the translation of the source into another programming language. Given a piece of legacy code and a library of program plan templates, understanding the code corresponds to building mappings from parts of the source code to particular program plans. The process of understanding a source code in a high-level programming language involves complex computation. ![]()
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