Satya Prasad, B. Indira Reddy & Krishna Mohan Gonuguntla International Journal of Software Engineering (IJSE), Volume (5) : Issue (1) : 2014 1 Software Process Control on Ungrouped Data: Log-Power Model Satya Prasad Ravi profrsp@gmail.com Associate Professor, Dept. of Computer Science & Engg., Acharya Nagarjuna University Andhrapradesh, India B. Indira Reddy Indira2259@yahoo.co.in Associate professor, St. Pauls college of Management & IT Hyderabad, India. Krishna Mohan Gonuguntla km_mm_2000@yahoo.com Reader, Dept. of Computer Science, P.B.Siddhartha College Andhrapradesh, India. Abstract Statistical Process Control (SPC) is the best choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of failures which is ungrouped data using an infinite failure mean value function of Log-Power model, which is Non-Homogenous Poisson Process (NHPP) based. The Maximum Likelihood Estimation (MLE) approach is used to estimate the unknown parameters of the model. Keywords: MLE, SPC, Log-Power, Ungrouped Data. 1. INTRODUCTION Many software reliability models have been proposed in last 40 years to compute the reliability growth of products during software development phase. These models can be of two types i.e. static and dynamic. A static model uses software metrics to estimate the number of defects in the software. A dynamic model uses the past failure discovery rate during software execution over time to estimate the number of failures. Various software reliability growth models (SRGMs) exist to estimate the expected number of total defects (or failures) or the expected number of remaining defects (or failures). The goal of software engineering is to produce high quality software at low cost. As, human beings are involved in the development of software, there is a possibility of errors in the software. To identify and eliminate human errors in software development process and also to improve software reliability, the Statistical Process Control concepts and methods are the best choice. SPC concepts and methods are used to monitor the performance of a software process over time in order to verify that the process remains in the state of statistical control. It helps in finding assignable causes, long term improvements in the software process. Software quality and reliability can be achieved by eliminating the causes or improving the software process or its operating procedures [1]. The most popular technique for maintaining process control is control charting. The control chart is one of the seven tools for quality control. Software process control is used to secure, that the