A PD ANN Machine Learning Framework for Reliability Optimization in Application Software

Abstract

Software plays a crucial role in industries and other day today activities and the main aim of the developer is to progress an application with the least number of errors so that it becomes a high dependability product. Expanding reliability by meeting out the expectations of the user turns on the quality of the software and all must be done within the agreed given time interval. Developers utilize different modules in developing an application and testing all those modules in a restricted time consumes much effort and affects the standard of the product if it was not tested correctly. Successful software modules must be certain in terms of their standard, satisfaction of the user and dependency. Software products must be dependent without having estimated errors. Software reliability models apply time-based models to recognize their faults under vital situations. In some models test effect has been applied to recognize the defects in the product and that is not practical for boundless testing time methods.