History essay help
In most cases, we were able to circumvent these limitations.
That is, in many cases, planner limitations do not reduce the practical expressivity of the specification language, but they do make specifications more verbose and awkward. For example, poor support for negative and existential conditions forced us to clutter our specification with many otherwise unnecessary declarations. Ideally, we should be able to write a PDDL specification once and use any PDDL-based planner to analyze it. PDDL specifications are inherently difficult to debug. If a specification author forgets, for example, to specify a necessary initial condition in the problem description, causing the problem to be unsolvable, the planner will simply say that it is unable to generate a plan. There is no good way to track down the cause of the problem. Even the most stable planners are fairly buggy and have limited documentation. We chose LPG-td and OPTIC for their relative maturity and stability, but while using them, history essay help we encountered many bugs and undocumented limitations.
Error messages were often unhelpful, and it can be difficult to tell whether a problem is caused by a specification error, a limitation of the planner, or an outright bug. When a planner encounters something its designers did not anticipate, it is as likely to crash with a segmentation fault as it is to display any useful explanation of the problem. However, the three projects had very different purposes and goals. In this summary, I compare the three pieces of work described in this chapter and explain their relevance to my thesis (and in particular the research claim of implementability). Thus, I do not consider it part of my thesis history essay help work, and I have not described it in any significant detail history essay help in this chapter. I have referenced this early implementation work in this chapter primarily for the sake of completeness, but it provides only weak support for the research claim of implementability. The findings of this work directly address the research claim of implementability by reporting and discussing a number of general challenges to architecture evolution tool implementation. The final piece of work described in this chapter was an attempt to use research on automated planning to support automatic generation of evolution paths, based on specifications of an initial architecture, a target architecture, a set of operators, a set of constraints, and an evaluation function to optimize. This work was narrowly focused on the question of the automatability of the approach, as opposed to more general implementation issues. Another relevant topic is software project planning. Finally, there is now a growing body of research on the topic of planning software architecture evolution, which section 7. For alternative views of the software architecture evolution research literature, see the work of Breivold et al.
They focus primarily on the code level and do not capture information about architectural structure.
Change or decay continues until it becomes 145 7 Related work more cost-effective to replace the program with a recreated version.
As a program evolves, its complexity increases and structure deteriorates unless work is done to reduce it. The global activity rate in a program evolution project is statistically invariant. The first two laws have particularly clear relevance here. These truths, which remain relevant today, justify and explain the need for principled, high-level evolution to keep systems relevant and coherent.
While these resources provide useful general guidance on effective project planning, they do not provide architects with domain-specific, situationally tailored guidance on how to structure an evolution. The first class of research answers the question of how to architect software that will be easy to evolve or adapt in the future. The second answers the question of how to determine the ease with which a software system can be evolved. Thus, the first is prescriptive and the second is descriptive. Various approaches have been taken to architecting evolvable software. Focusing particularly on the domain of embedded systems, America et al. The question of how to architect evolvable software is an important one, but it is useful chiefly for the construction of new systems.
There are a variety of evaluative approaches as well. There are also a number of economic approaches to evaluating the evolvability of an architecture. What all this work on evolvability has in common is that it is concerned specifically with ensuring that the future evolution of a system will be easy, not with planning the details of how such an evolution will be carried out. That is, both areas of work are concerned with evolvability as a quality attribute rather than evolution as an activity. There have arisen a number of different perspectives on the topic of planning architecture evolution, which I summarize under the following subheads: Capturing architectural transformations. Finally, these approaches lack empirical validation.
However, this was a small example in laboratory conditions, not an observation of real-world software engineering practice. Conceptually, architectural tactics and evolution operators are quite different. An evolution operator represents some real-world operation carried history essay help out over time in the course of a software system evolution.
For example, an availability tactic based on incorporating redundancy in an architectural design might give rise to an evolution operator that adds a failover component. They use the term evolution style to denote a pattern for updating a component-based architecture. They provide a formal approach based on a three-tiered conceptual framework. However, unlike the work described in this dissertation, they do not explicitly characterize or reason about the space of architecture paths, or apply utility-oriented evaluation to selecting appropriate paths. In addition, they seem to have relied exclusively on fictitious examples rather than observing real evolutions.