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In software engineering, the laws of software evolution refer to a series of laws that Lehman and Belady formulated starting in 1974 with respect to software evolution. [1] [2] The laws describe a balance between forces driving new developments on one hand, and forces that slow down progress on the other hand.
Key features of both passive and active adaptive management are: Iterative decision-making (evaluating results and adjusting actions on the basis of what has been learned) Feedback between monitoring and decisions (learning) Explicit characterization of system uncertainty through multi-model inference; Bayesian inference
For this reason, developers implement techniques of adaptation into the system in order to react to changing conditions as fast as possible. The example application scenario clearly shows an important distinction concerning such adaptation techniques: the differentiation between manually and automatically performed adaptation processes.
A simplified version of a typical iteration cycle in agile project management. The basic idea behind this method is to develop a system through repeated cycles (iterative) and in smaller portions at a time (incremental), allowing software developers to take advantage of what was learned during development of earlier parts or versions of the system.
Domain adaptation is a specialized area within transfer learning. In domain adaptation, the source and target domains share the same feature space but differ in their data distributions. In contrast, transfer learning encompasses broader scenarios, including cases where the target domain’s feature space differs from that of the source domain(s).
Often, two or more species co-adapt and co-evolve as they develop adaptations that interlock with those of the other species, such as with flowering plants and pollinating insects. In mimicry , species evolve to resemble other species; in mimicry this is a mutually beneficial co-evolution as each of a group of strongly defended species (such as ...
The Plan-Do-Check-Act (PDCA) cycle, often referred to as the Deming Cycle, is a scientific method for testing concepts and putting changes into action that helps make better decisions. The focus on small-scale plan testing initially, which lowers the possibility of broad problems and encourages fault avoidance, is what distinguishes PDCA.
The plan–do–check–act cycle is an example of a continual improvement process. The PDCA (plan, do, check, act) or (plan, do, check, adjust) cycle supports continuous improvement and kaizen. It provides a process for improvement which can be used since the early design (planning) stage of any process, system, product or service.