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