Title :
Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction
Author :
Kagdi, Huzefa ; Maletic, Jonathan I.
Author_Institution :
Kent State Univ., Kent
Abstract :
The paper advocates the need for the investigation and development of a software-change prediction methodology that combines the change sets estimated from software dependency analysis (via single-version analysis) and the actual change sets found in software version histories (via multiple-version analysis). Traditionally prescribed methodologies such as Impact Analysis (IA) are based on the former, whereas a more recent methodology, mining software repository (MSR), is based on the latter. The research hypothesis is that combining these two methodologies will result in an overall improved support for software-change prediction.
Keywords :
configuration management; data mining; data warehouses; management of change; software development management; MSR method; change sets; evolutionary dependencies; mining software repository; multiple-version analysis; single-version analysis; software dependency analysis; software version histories; software-change prediction; Collaborative software; Computational efficiency; Computer science; History; Open source software; Performance analysis; Software performance; Software systems; State estimation; Unified modeling language;
Conference_Titel :
Mining Software Repositories, 2007. ICSE Workshops MSR '07. Fourth International Workshop on
Conference_Location :
Minneapolis, MN
Print_ISBN :
0-7695-2950-X