Practical Bayes Approach For Detection of Software Defects in Cloud Services

Now a days Cloud computing plays a vital role in the IT enterprise. Statistical Process Control cloud charts sense routine variances and their root causes are identified based on the differential profiling strategy. Most of the manual overhead incurred in detecting the software variances and the analysis time are reduced to a larger extent but detailed analysis of profiling data are not performed in most of the cases. At the same time, Trusted Computing Base (TCB) of a computing node does not achieve the scalability measure. This work, a Practical Bayes approach studies the problem of detecting software variances and ensures scalability by comparing information at the current time to historical data. Gen Prog uses an extensive structure of genetic programming to develop a progrant variant that retains essential functionality but it is not vulnerable to a known deficiency in cloud. The existing software testing suite identifies program defects in cloud environment. Delta debugging and Structural differencing algorithms minimize the dissimilarity among variant and the original program in terms of minimum repair. Subsequently, Defect Localization based on Band (DLB) mechanism is introduced to overcome the defects and rank the different acceptable patches. Research Scholar, Department of Computer Scienos, Karpagam University, Coimbatore 641 120 Tamil Nadu, India. Email: nethaj.babu@gmail.com. Mobile: +91-9962070800 Assistant Professor, Computer Science Department, Periyar University, Salem, 636011, India Email: ccsekar@gmail.com. Mobile: +91-9994599967