Abstract:
With the evolution of the software industry, the growing software complexity led
to the increase in the number of software faults. According to the study, the
software faults are responsible for many unplanned system outages and a ects the
reputation of the company. Many techniques are proposed in order to avoid the
software failures but still software failures are common. Software fault prediction is
the process, which predicts about the fault proneness of the software module. This
process is based on some previous data (if available) and certain learning models.
The prediction of the accurate location of faults can boost the testing process
and allows the developers to focus on the critical modules that may account for
the maximum number of faults. Many software faults and failures are outcomes
of a phenomenon, called software aging. In this work, we have presented the
use of various ensemble models for development of approach to predict the Aging
Related Bugs (ARB). A comparative analysis of di erent ensemble techniques,
bagging, boosting and stacking have been presented. The experimental study has
been performed on the LINUX and MYSQL bug datasets collected from Software
Aging and Rejuvenation Repository.
