Jie Xu

Jie Xu, Danny Ho, Luiz Fernando Capretz

Open Source Software (OSS) has been a popular form in software development. In this paper, we use statistical approaches to derive OSS quality estimation models. Our objective is to build estimation models for the number of defects with metrics at project levels. First CATREG (Categorical regression with optimal scaling) is used to obtain quantifications of the qualitative variables. Then the independent variables are validated using the stepwise linear regression. The process is repeated to acquire optimal quantifications and final regression formula. This modeling process is performed based on data from the OSS communities and is proved to be practically valuable.

Jie Xu, Luiz Fernando Capretz, Danny Ho

Software quality assurance has been a heated topic for several decades, but relatively few analyses were performed on open source software (OSS). As OSS has become very popular in our daily life, many researchers have been keen on the quality practices in this area. Although quality management presents distinct patterns compared with those in closed-source software development, some widely used OSS products have been implemented. Therefore, quality assurance of OSS projects has attracted increased research focuses. In this paper, a survey is conducted to reveal the general quality practices in open source communities. Exploratory analysis has been carried out to disclose those quality related activities. The results are compared with those from closed-source environments and the distinguished features of the quality assurance in OSS projects have been confirmed. Moreover, this study suggests potential directions for OSS developers to follow.

Jie Xu, Danny Ho, Luiz Fernando Capretz

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.