Titlebook: Constraint-Based Design Recovery for Software Reengineering; Theory and Experimen Steven G. Woods,Alexander E. Quilici,Qiang Yang Book 1998

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Michael J. Clay Ph.D.,Jenna N. Albersngineering but is difficult to obtain due to the complexity of software, the specialized knowledge used to construct it, and the lack of existing understanding aids. As a result, there is a need for tools that can help programmers obtain this understanding. It is particularly crucial to have tools t

linear

L. Jehel,R. Arnal,D. Carmelo,N. Howardts are the Concept Recognizer, DECODE, GRASPR, and Unprog. While they differ considerably in the details, they view the plan recognition problem as locating a set of items in the code that meet certain constraints, and they tend to to use heuristics to reduce the combinatorics involved in plan recog

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https://doi.org/10.1007/978-3-319-26282-6 algorithms tend to have different behavior in different problem domains, we have run a series of experiments to try to find a solving strategy whose performance is reasonable on small programs (those containing less than 1,500 lines of source code). We have then taken the best strategy and explored

Boycott

Palter

Effect of Economic Crises on Suicide Ratescognition techniques to the program understanding problem. Curiously, however, program understanding researchers have not done so, and they have not explained why they have avoided the use of these plan recognition algorithms. The problem is that treating program understanding as plan recognition is

hazard

Gustavo Zurita,José A Pino,Nelson Baloianrward use of MAP-CSP, and there is empirical evidence that MAP-CSP is an efficient mechanism for recognizing individual plans. Unfortunately, it is not clear that the Layered MAP-CSP approach scales with the size of the plan library, nor is it obvious how Layered MAP-CSP can be effectively extended