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

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