International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 3, March 2015, PP 21-24 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org ©ARC Page 21 Burr Type III Software Reliability with SPC-An Order Statistics Approach K. Sobhana Department of Computer Science Krishna University Machilipatnam, Andhra Pradesh,India msobhana@yahoo.com Dr. R. Satya Prasad Dept. of Computer Science & Engg Acharya Nagarjuna University Guntur, Andhra Pradesh (India) prof_rsp@gmail.com Abstract: In the past few decades research on software reliability has been conducted and several software reliability growth models have been developed for estimating software reliability. Order Statistics is an approach for estimating software reliability for time domain data based on NHPP with a distribution model. This paper presents the Burr Type III model as a software reliability growth model and derives the expressions for an efficient reliability function using order statistics. Statistical Process Control (SPC) can be used to monitor the software reliability process and thereby improve the software quality. Control Charts are one of the powerful SPC tools to analyze the failure frequency. It is proposed that the SPC can be applied to monitor the software failure process of Burr Type III based NHPP. Keywords: Software reliability; Burr type III; Order Statistics; Statistical Process Control;NHPP 1. INTRODUCTION Software reliability is one of the most important characteristics of software quality. Reliable software systems can be produced and maintained by employing quality measurement and management technologies during the software life cycle. Software Reliability is the probability of failure free operation of software in a specified environment during specified time [1]. The monitoring of Software reliability process is a far from simple activity. In recent years, several authors have recommended the use of SPC for software process monitoring. A few others have highlighted the potential pitfalls in its use[2]. The main thrust of the paper is to formalize and present an array of guidelines in a disciplined process with a view to helping the practitioner in putting SPC to correct use during software process monitoring. Over the years, SPC has come to be widely used among others, in manufacturing industries for the purpose of controlling and improving processes. Our effort is to apply SPC techniques in the software development process so as to improve software reliability and quality [3]. It is reported that SPC can be successfully applied to several processes for software development, including software reliability process. SPC is traditionally so well adopted in manufacturing industry. In general software development activities are more process centric than product centric which makes it difficult to apply SPC in a straight forward manner. The utilization of SPC for software reliability has been the subject of study of several researchers. A few of these studies are based on reliability process improvement models. They turn the search light on SPC as a means of accomplishing high process maturities. Some of the studies furnish guidelines in the use of SPC by modifying general SPC principles to suit the special requirements of software development [3] (Burr and Owen[4]; Flora and Carleton[5]). It is especially noteworthy that Burr and Owen provide seminal guidelines by delineating the techniques currently in vogue for managing and controlling the reliability of software. Significantly, in doing so, their focus is on control charts as efficient and appropriate SPC tools. It is accepted on all hands that Statistical process control acts as a powerful tool for bringing about improvement of quality as well as productivity of any manufacturing procedure and is particularly relevant to software development also. Viewed in this light, SPC is a method of process management through application of statistical analysis, which involves and includes the defining, measuring, controlling, and improving of the processes[6]. 2.1. Model Development A. NHPP Model Software reliability probabilistic models can be classified as Markovian models and fault counting models. In Markovian model a Markov process represents the failure process. In fault counting model the failure process is described by stochastic process like Homogeneous Poisson Process (HPP), Non Homogeneous Poisson Process (NHPP) and