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In addition to sequential composition of transformations, we allow simple control flow mechanisms such as loops in our operator description 14 2.
In this case, an operator must include the structural changes for each view that is under consideration.
In addition to transformations, an operator essay writing service us can include preconditions and analysis information. Preconditions, which describe the conditions that must be true prior to executing the transformation, are expressed as architectural constraints. A simple precondition for this operator might be: c must not already be in 15 2 Approach the cloud to begin with. When multiple views are under consideration, different preconditions can be specified for each view. The example in figure 1 contains only a single precondition, requiring that the component to be wrapped is of type LegacyComponent. If desired, we could add further preconditions to impose additional requirements—for example, enforcing restrictions on the types of ports that the legacy component has or the kinds of connectors to which it is attached.
Finally, analytical information may be included to support evolution analyses and constraints. For example, an operator can include information like the effort required to carry it out, the cost of carrying it out, its security implications, or the risks involved. Analysis information is less structured than the transformations and preconditions section. The kinds of analysis information included with an operator are dependent on the analyses that the evolution style supports.
The operator in figure 1 contains a single item of analytical information, an estimate of the amount of effort required to wrap a legacy component as a service. This information could be used by an evaluation function that estimates the total effort of an evolution path by adding up the effort predictions of its constituent operators. In figure 1, the effort estimate is simply specified as a number indicating a fixed number of hours of effort required, but we could also define a value that is dependent on the parameters of the operator, or we could specify a range of values to indicate uncertainty. Given a set of operators defined in this way, we can use them to define evolution transitions among the evolution states. An evolution transition is composed of one or several operators and describes the architectural transformations necessary to evolve the system from one state to another. An evolution path is a plan described in such a way. To understand the space of alternatives under consideration, we construct 16 2.
Each node in the graph is a complete architectural representation of the system. The edges of the graph custom essays writing service represent possible evolutionary transitions. This graph has only three paths: 1-2-4-6,1-3-4-6, and 1-3-5-6.
The task of the software architect is to select an optimal evolution path —a way of evolving the system from the initial architecture to the target architecture. An evolution path thus captures a particular plan of evolution. Given an evolution graph as defined above, an evolution path is simply a path in the graph-theoretical sense: an open walk from the node corresponding to the initial architecture to that corresponding to the target architecture. I will clarify the meaning of optimal in section 2. This graph-theoretical formulation of software architecture evolution is a simple one, but it provides a useful foundation for reasoning about potential evolution plans and the distinctions among them. The next two sections discuss constraints and evaluation functions. Examples of evolution path constraints from various domains are: 1.
Once a server is placed in the Boston data center, it must not be removed (domain: data migration).
The billing subsystem will not be removed until a controller component is introduced (domain: e-commerce).
Once appropriate constraints for a domain are defined, many of the paths in an evolution graph can be eliminated automatically by constraint enforcement. Formally, a path constraint is a predicate (Boolean-valued function) over evolution paths. Thus, a constraint judges each evolution path to be either valid or invalid with respect to the rule of evolution that the constraint encodes.
Path constraints may be specified in an augmented version of linear temporal logic (LTL).
Temporal logic is a natural choice, since path constraints are temporal propositions.
In addition, the interpretation of temporal formulas with respect to an evolution graph is straightforward. An evolution graph can be naturally viewed as a Kripke structure whose states are simply the evolution states and whose transitions are simply the evolution transitions. This allows temporal formulas to be defined and interpreted in the usual way. Chapter 3 will describe our path constraint specification language in full detail, but here I give a brief introduction. For example, consider constraint 3 from the list above.
If we try to express this constraint in LTL, we quickly encounter a problem. LTL, however, cannot capture predicates relating multiple different states.
Because of the finite nature of paths, it is possible to check whether a given evolution path satisfies a given set of evolution constraints. Thus verification of path constraints can be automated. Chapter 3 will treat the path constraint language in detail, discussing a number of related logics and proving results about the computational complexity of path constraint verification. Figure 3 shows a constraint based on the example of section 1. However, the real benefit of defining paths of evolution is to compare them and decide which path is the best to take. This is the purpose of evolu tion path evaluation functions. Formally, constraints and evaluation functions are close kin: an evolution constraint is a Boolean-valued function (i. At the top are a path constraint (based on the example of section 1. The diagram depicts a fragment of an evolution path that violates the constraint. Not shown are the definitions of clientConnectedToUnifiedDash- board and cloudUnchanged, which are predicates over architecture descriptions and can be defined by conventional means (e. Evaluation functions facilitate path selection by assigning to each evolution path a value (in this case, an estimate of the total cost of the path). Evaluation functions help software architects to determine whether a path satisfies business and management goals. In general, evaluation functions will depend on attributes specific to a particular business context: (a) the qualities essay writing service us of concern (cost, functionality, time, etc. Ultimately, the goal of evolution graph analysis is to select an optimal path. This requires the definition of an evaluation function that provides an evaluation of the overall goodness of each path, so that the paths may be compared. Thus, an evaluation function for utility might be a composite of evaluation functions essay writing service us for attributes such as time, cost, and risk, chosen and weighted appropriately for the problem under consideration.
Another way of developing a utility function is with a cost-benefit approach.
Instead, we assume that evaluation functions are defined as functions in a general-purpose programming language, which take the evolution model as input and produce a numeric value as output. Indeed, many of the concerns that arise during evolution planning are domain-specific.