Abstract: In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.
Abstract: Software Architecture is the basic structure of
software that states the development and advancement of a software
system. Software architecture is also considered as a significant tool
for the construction of high quality software systems. A clean design
leads to the control, value and beauty of software resulting in its
longer life while a bad design is the cause of architectural erosion
where a software evolution completely fails. This paper discusses the
occurrence of software architecture erosion and presents a set of
methods for the detection, declaration and prevention of architecture
erosion. The causes and symptoms of architecture erosion are
observed with the examples of prescriptive and descriptive
architectures and the practices used to stop this erosion are also
discussed by considering different types of software erosion and their
affects. Consequently finding and devising the most suitable
approach for fighting software architecture erosion and in some way
reducing its affect is evaluated and tested on different scenarios.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.