By Steven G. Woods
The nice problem of opposite engineering is recuperating layout info from legacy code: the concept that restoration challenge. This monograph describes our examine attempt in attacking this challenge. It discusses our idea of the way a constraint-based method of application plan reputation can successfully extract layout recommendations from resource code, and it information experiments in proposal restoration that aid our claims of scalability. Importantly, we current our types and experiments in enough aspect in order to be simply replicated. This ebook is meant for researchers or software program builders interested in opposite engineering or reengineering legacy platforms. in spite of the fact that, it may possibly additionally curiosity these researchers who're utilizing plan acceptance recommendations or constraint-based reasoning. we predict the reader to have an inexpensive machine technological know-how heritage (i.e., familiarity with the fundamentals of programming and set of rules analysis), yet we don't require familiarity with the fields of opposite engineering or synthetic intelligence (AI). To this finish, we conscientiously clarify all of the AI suggestions we use. This ebook is designed as a reference for complex undergraduate or graduate seminar classes in software program engineering, opposite engineering, or reengineering. it might additionally function a supplementary textbook for software program engineering-related classes, comparable to these on application realizing or layout restoration, for AI-related classes, akin to these on plan popularity or constraint delight, and for classes that conceal either subject matters, reminiscent of these on AI functions to software program engineering. association The ebook includes 8 chapters.
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Additional resources for Constraint-Based Design Recovery for Software Reengineering: Theory and Experiments
Signal ? Read ? Test compares ? Signal & ? Stop-Constant ? Test does not perform input ? Process does not perform input ? Process does not define? Signal ? 12. The "Read-Process-Loop" STIMP. 2. Exact graph matching. Each STIMP from the plan library is compared against each of the sub-HMODELs, starting from the top of the decomposition "tree". The comparison algorithm has these steps: 1. Match entries in the control-flow graph part of a sub-HMODEL against the STIMP using depth-first search. This results in zero or more bindings between subHMODEL entries and the STIMP control-flow entries (initial candidate instances of the STIMP).
Each grammar rule involving I and meeting known constraints involving I and P) and where I is in the "immediately needed" set, make a new item extending P with I and add it to the agenda. If I is partial, then for each complete item C in the chart that extends I where I is in the "immediately needed" set of C, make a new item extending I with C. , evaluate constraints not evaluated during the extending process, combine share-equivalent ftowgraphs, and so on). 9. GRASPR's chart-parsing plan recognition algorithm.
This leaves another important open question . Does there exist a unifying framework for describing these approaches that supports empirical and analytical comparisons of their behavior? 3 PROGRAM UNDERSTANDING AND CONSTRAINT SATISFACTION While previous program understanding approaches differ in many ways, they all depend on constraints. This suggests applying techniques for solving constraint satisfaction problems (CSPs) to program understanding.