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Register a new userThe proposal of improved component selection framework
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Added by
M. Rizwan Jameel Qureshi Dr.
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Component selection is considered one of hard tasks in Component Based Software Engineering (CBSE). It is difficult to find the optimal component selection. CBSE is an approach that is used to develop a software system from pre-existing software components. Appropriate software component selection plays an important role in CBSE. Many approaches were suggested to solve component selection problem. In this paper the component selection is done by improving the integrated component selection framework by including the pliability metric. Pliability is a flexible measure that assesses software quality in terms of its components quality. The validation of this proposed solution is done through collecting a sample of people who answer an electronic questionnaire that composed of 20 questions. The questionnaire is distributed through social sites such as Twitter, Facebook and emails. The result of the validation showed that using the integrated component selection framework with pliability metric is suitable for component selection.
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Software testing is normally used to check the validity of a program. Test oracle performs an important role in software testing. The focus in this research is to perform class level test by introducing a testing framework. A technique is developed to generate test oracle for specification-based software testing using Vienna Development Method (VDM++) formal language. A three stage translation process, of VDM++ specifications of container classes to C++ test oracle classes, is described in this paper. It is also presented that how derived test oracle is integrated into a proposed functional testing framework. This technique caters object oriented features such as inheritance and aggregation, but concurrency is not considered in this work. Translation issues, limitations and evaluation of the technique are also discussed. The proposed approach is illustrated with the help of popular triangle problem case study.
Design patterns being applied more and more to solve the software engineering difficulties in the object oriented software design procedures. So, the design pattern detection is widely used by software industries. Currently, many solutions presented to detect the design pattern in the system design. In this paper, we will propose a new one which first; we will use the graph implementation to implement both the system design UML diagram and the design pattern UML diagram. Second, we will implement the edges for each one of the both two graphs in a set of 4-tuple elements. Then, we will apply a new inexact graph isomorphic algorithm to detect the design pattern in the system design.
Suppose we have $n$ different types of self-replicating entity, with the population $P_i$ of the $i$th type changing at a rate equal to $P_i$ times the fitness $f_i$ of that type. Suppose the fitness $f_i$ is any continuous function of all the populations $P_1, dots, P_n$. Let $p_i$ be the fraction of replicators that are of the $i$th type. Then $p = (p_1, dots, p_n)$ is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fishers fundamental theorem of natural selection. We compare it to Fishers original result as interpreted by Price, Ewens and Edwards.
We analyze the orthogonal greedy algorithm when applied to dictionaries $mathbb{D}$ whose convex hull has small entropy. We show that if the metric entropy of the convex hull of $mathbb{D}$ decays at a rate of $O(n^{-frac{1}{2}-alpha})$ for $alpha > 0$, then the orthogonal greedy algorithm converges at the same rate. This improves upon the well-known $O(n^{-frac{1}{2}})$ convergence rate of the orthogonal greedy algorithm in many cases, most notably for dictionaries corresponding to shallow neural networks. Finally, we show that these improved rates are sharp under the given entropy decay assumptions.
Power decoding is a partial decoding paradigm for arbitrary algebraic geometry codes for decoding beyond half the minimum distance, which usually returns the unique closest codeword, but in rare cases fails to return anything. The original version decodes roughly up to the Sudan radius, while an improved version decodes up to the Johnson radius, but has so far been described only for Reed--Solomon and one-point Hermitian codes. In this paper we show how the improved version can be applied to any algebraic geometry code.
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