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Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices.In order to overcome these challenges, this paper aims to define the term ‘usability’ using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and nearest neurons from among the 1-neighborhood of the winner neuron, and also the winning frequency of each neuron are found out and taken into account while updating the weight. This new SOM is applied to various input data sets and the learning performance is evaluated using three standard measurements. It is confirmed that modified SOM obtained a far better result and better effective mapping as compared to the conventional SOM, which reflects the input data distribution.ª 2014 Production and hosting by Elsevier B.V. on behalf

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