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I would like to acknowledge here the contribution of Ashutosh 125 6 Tooling In motivating this thesis work in section 1. The problem with such an approach is that it imposes a substantial burden on the architect. The architect must explicitly define the candidate evolution paths and specify the evolutionary transitions that occur within each such path. In our original prototype, Aivol, this was done through explicit definition of intermediate states. The JPL case study and the follow-on MagicDraw plug-in work streamlined this process by allowing intermediate states to be defined in terms helping writing essay of the initial state by applying evolution operators. But even in this streamlined process, definition of intermediate states can be an onerous task in a scenario with many candidate evolution paths, and numerous transitions within each path. A better approach would be to generate these evolution paths top rated essay writing websites automatically. While this would alleviate the burden on the architect, it introduces a new difficulty: determining how to compose the operators together so as to generate the target architecture from the initial architecture. Clearly an undirected brute-force search for an optimal path would be unwise. In this section, I describe our attempt to apply existing approaches and tools from automated planning to the architecture evolution path generation problem. A Pandey, with whom I collaborated on this research.
For a discussion of alternative definitions, including some that are more computationally oriented, see Ghallab et al. To solve a planning problem, a planner must receive a specification of the problem in a standard format. A number of specification languages for planning problems have been devised, but by far the most popular—the lingua franca of automated planning—is the Planning Domain Description Language. These qualities, along with its feature set, made PDDL a good choice for our work. All three of these are extremely useful for modeling architecture evolution problems, as we will see later.
There have subsequently been further additions to the language, such as the introduction of derived predicates in PDDL2. While these features would certainly have been useful to us, they are not as broadly supported by planners, so we chose to target PDDL2. Thus, a domain description can be shared across multiple planning problems in the same domain. Both the domain file and the problem file are expressed in a Lisp-like syntax, as a list of parenthesized declarations.
Rather, predicates, functions, and actions can specify the types that they govern. A durative action additionally specifies its duration. A planner takes a domain description and problem description as input and produces a plan as output—a timed list of actions (with parameters specified) that achieves the specified goals. With the problem framed in this manner, we can apply automated planning tools to the task of generating evolution paths.
In the remainder of this section, 1 will describe how an architecture evolution problem can be translated into a planning problem expressed in PDDL.
We can define component and connector types as types in the PDDL domain description, then define the components and connectors themselves as PDDL objects of the defined types. Finally, the relationships among the components and connectors 128 6. An extremely simple software architecture and its PDDL representation. The specification of the initial architecture will appear within the :init block, which defines the initial conditions, and the act essay help specification of the target architecture will appear within the :goal block, which defines the goals.
An evolution operator, of course, corresponds to a PDDL action. But how can we actually capture an evolution operator as an action using the specification facilities that PDDL provides? For example, a wrap legacy component operator will take as a parameter the component to wrap. In PDDL, an action likewise specifies its parameters. These are expressed as a sequence of elementary architectural changes such as delete component or attach connector. These are specified with the temporal annotations at start, at end, and over all. One subtlety worth noting is that PDDL does not permit actions to create new top rated essay writing websites objects (nor destroy existing ones). This is significant because many evolutions entail the creation of new architectural elements, or the decommissioning of existing ones. As a result, in an evolution that may involve creation of new elements, we must declare some potential objects that do not exist in the initial architecture but may be used to stand in for elements created during the evolution. In this case, we can define an isReal predicate that is false for such potential objects and becomes true when an action creates a new architectural element out of a potential object. Such approaches have substantial limitations and are rather cumbersome, and Frank et al.
Path constraints are perhaps the most challenging element of an architecture evolution problem to translate into PDDL. In chapter 3,1 described an extension of linear temporal logic that is particularly suitable for expressing evolution path constraints.
This process is conceptually complex and encumbers the specification with numerous state variables. Therefore, I leave to future work the challenge of extending top rated essay writing websites this compilation process to the augmented version of LTL that we use to capture path constraints. To avoid such conceptual complexities in this work, we took a pragmatic approach: we characterized certain restricted classes of path constraints (with an eye toward the kinds of constraints that will arise in the example of section 6. The simplest possible kind of constraint is one that must hold continuously through the entire duration of the evolution (e. Despite their simplicity, these constraints are quite common. Such a constraint amounts to an architectural constraint that persists through an evolution.