An integrated software quality model and its adaptability within monolithic and virtualized cloud environments

Kiruthika, J. (2014) An integrated software quality model and its adaptability within monolithic and virtualized cloud environments. (PhD thesis), Kingston University, .


One fundamental problem in current software development life cycles, particularly in distributed and non-deterministic environment, is that software quality assurance and measurements do not start early enough in the development process. Recent research work has been trying to address this problem by using software quality assurance (SQA) measurement frameworks. However, before such frameworks are developed and adopted there is a need to have a clear understanding and to define what is meant by quality. To help this definition process, numerous approaches and quality models have been developed. Many of the early quality models have followed a hierarchical approach with little scope for expansion. More recent models have been developed that follow a 'Define your own' approach. Although an improvement, difficulties arise when comparing quality across projects, due to their tailored nature. The aim of this project is to develop a new generic framework to software quality assurance which addresses the problems of existing approaches. The proposed framework will blend various quality measurement approaches and will provide statistical, probabilistic and subjective measurements for both required and actual quality. Unlike existing techniques, autodidactic mechanisms are incorporated which can be used to measure any software entity type. This however should include the measurements of actual quality using software quality factors that are based on experimental measurements i.e., not only on the subjective view of stakeholders. Moreover the framework should also include the conversion into software measurements of historical reports/data that can be extracted from problem reporting systems such date of problem identification, source of report, critical tendencies of report, cause of problem etc. and other available statistical information. The proposed framework retains the knowledge about software defects and their impact on quality, and has the capacity to add new knowledge dynamically.

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