File Name: what is fine and coarse granularity in software testing in .zip
While sensory processes are tuned to particular features, such as an object's specific location, color or orientation, visual working memory vWM is assumed to store information using representations, which generalize over a feature dimension.
- Our Approach to Testing a Large-Scale C++ Codebase
- Subscribe to RSS
- Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining
Feature-based opinion mining from product reviews is a difficult task, both due to the high semantic variability of opinion expression, as well as because of the diversity of characteristics and sub-characteristics describing the products and the multitude of opinion words used to depict them.
Our Approach to Testing a Large-Scale C++ Codebase
Granularity also called graininess , the condition of existing in granules or grains , refers to the extent to which a material or system is composed of distinguishable pieces. It can either refer to the extent to which a larger entity is subdivided, or the extent to which groups of smaller indistinguishable entities have joined together to become larger distinguishable entities. Coarse-grained materials or systems have fewer, larger discrete components than fine-grained materials or systems. The concepts granularity , coarseness , and fineness are relative; and are used when comparing systems or descriptions of systems. Note that, although the modifying terms, fine and coarse are used consistently across all fields, the term granularity is not. A fine-grained description of a system is a detailed, exhaustive, low-level model of it.
Subscribe to RSS
Back to Search. Give Feedback. Description Conflict and dependency analysis CDA of graph transformation has been shown to be a versatile foundation for understanding interactions in many software engineering domains, including software analysis and design, model-driven engineering, and testing. In this paper, we propose a novel static CDA technique that is multi-granular in the sense that it can detect all conflicts and dependencies on multiple granularity levels. Specifically, we provide an efficient algorithm suite for computing binary, coarse-grained, and fine-grained conflicts and dependencies: Binary granularity indicates the presence or absence of conflicts and dependencies, coarse granularity focuses on root causes for conflicts and dependencies, and fine granularity shows each conflict and dependency in full detail. Doing so, we can address specific performance and usability requirements that we identified in a literature survey of CDA usage scenarios.
Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining
It outlines the computers with multiple processing elements that can perform the same operation on multiple data points simultaneously. It is actually related when a larger entity is subdivided into various parts. For example, a plot is broken into yards for much finer granularity than just saying a plot. Attention reader!