An Empirical Study on the Procedure to Derive Software Quality Estimation Models
This work addresses software quality assurance for development management, but it appears incremental as it builds on existing methods without introducing a major breakthrough.
The paper tackles the problem of early-stage software quality estimation by proposing a general procedure to derive models, using statistical and machine learning techniques to verify software metrics and adopting a neuro-fuzzy approach to improve accuracy, with empirical validation on ISBSG repository data.
Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value.