International Society of Science and Applied Technologies |
|
Software Quality Prediction Based on Generalized Linear Models | ||||
Author | Shinji Inoue
|
|||
Co-Author(s) | Shigeru Yamada
|
|||
Abstract | Statistical analysis approaches are often conducted for software quality prediction based on software process data collected from an actual software development project. As one of the main approaches is to use multiple regression analysis where the objective variable represents the number of faults detected in the system testing and the software process factors are treated as the explanatory variables. However, this approach is unsuitable because the objective variable is permitted to take the negative value in the multiple regression approach. We propose another statistical approaches for overcoming the problem by applying the generalized linear modeling approach to the software quality prediction. Further we check the prediction and fitting performance of our approaches with existing approach by using actual software process data.
|
|||
Keywords | Software quality/reliability, Software process data, Statistical analysis, Generalized linear models, Model comparisons | |||
Article #: 2194 |
August 6-8, 2015 - Philadelphia, Pennsylvia, U.S.A. |