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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.
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 t
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
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 popula
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 >
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 de