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

無法破譯

手銬

Improving Our Constraint-Based Approach,rward 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

壓迫

MIR

Introduction,rstanding aids. As a result, there is a need for tools that can help programmers obtain this understanding. It is particularly crucial to have tools that can extract design concepts. These tools can extract these design concepts using a pattern-based approach, with optional guidance from the user.

disparage

ARENA

Additional Experiments with Concept Recovery,proach. We now report on experiments with programs based on real-world C code. While not definitive, these new experiments support our earlier results and have led to an improved constraint satisfaction algorithm that provides significant improvements in the scalability of our approach.

浪費時間

Conclusions, approach to program understanding out of the lab and into commercial practice. Despite its shortcomings, however, our work is an example both of the power of constraint satisfaction techniques and of the experimental approach in software engineering research.

formula

https://doi.org/10.1007/978-3-319-26282-6 its performance to see how it scales to larger programs and larger plans, and how its performance is affected by different component distributions. Along the way, we have compared MAP-CSP to a constraint-based implementation of an existing program understanding algorithm.

放逐

油氈

1384-6469 escribes our research effort in attacking this problem. It discusses our theory of how a constraint-based approach to program plan recognition can efficiently extract design concepts from source code, and it details experiments in concept recovery that support our claims of scalability. Importantly,