By Steven G. Woods
The nice problem of opposite engineering is improving layout info from legacy code: the concept that restoration challenge. This monograph describes our examine attempt in attacking this challenge. It discusses our concept of ways a constraint-based method of software plan reputation can successfully extract layout strategies from resource code, and it information experiments in notion restoration that help our claims of scalability. Importantly, we current our types and experiments in enough aspect with a purpose to be simply replicated. This booklet is meant for researchers or software program builders excited about opposite engineering or reengineering legacy platforms. in spite of the fact that, it may well additionally curiosity these researchers who're utilizing plan popularity ideas or constraint-based reasoning. we think the reader to have a cheap laptop technology history (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 the entire AI ideas we use. This booklet 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 software realizing or layout restoration, for AI-related classes, resembling these on plan popularity or constraint delight, and for classes that conceal either subject matters, akin to these on AI purposes to software program engineering. association The ebook contains 8 chapters.
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Extra info for Constraint-Based Design Recovery for Software Reengineering: Theory and Experiments
One other complication shared by the Concept Recognizer and DECODE is that, for efficiency, they both interleave evaluating constraints with binding components to the program's syntax tree. DECODE varies from the Concept Recognizer in that it provides an explicit static ordering for constraints and forms combinations of components only as they become necessary to evaluate these constraints. In addition, DECODE treats partially-specified constraints as queries. , some Not-Equal). In this case, DECODE follows the data-dependencies on the variable from the known component and returns the set of components of the given type that are dependent on that variable (in essence, computing the instance set for that type as the constraint is evaluated).
Its strategy is straightforward: run through the program tree, and whenever a component is an index for a plan and its indexing constraints succeed, match the remaining pieces of that plan against the code and evaluate the constraints on the partial plan instances formed by the matching process. In addition, whenever a plan is recognized and implies another plan, attempt to match the additional components and evaluate the additional constraints. Then for each plan recognized, recursively see if it indexes any plans in the library.
DECODE extends the Concept Recognizer's components and constraints representation to include an explicit index that suggests when a plan should be considered (as a result of matching the plan's index against program pieces or previously recognized sub-plans). Indices include an "indexing plan component" and a set of constraints. To state the indexing process briefly, whenever an indexing plan component of an index is located in the source, the constraints of the index are evaluated (which may in turn involve trying to match additional plan components).