Monitoring Software Reliability using Statistical Process control: An MMLE approach
R. Satya Prasad, Bandla Sreenivasa Rao, R. R. L. Kantam
TL;DR
This paper introduces a novel approach combining MMLE and SPC charts to estimate and monitor software reliability using exponential distribution and inter-failure data, providing a non-iterative analytical estimation method.
Contribution
It proposes a new MMLE-based scheme integrated with SPC charts for real-time software reliability monitoring, enhancing accuracy and simplicity over existing iterative methods.
Findings
Effective estimation of software reliability parameters.
SPC charts successfully detect process control status.
Non-iterative analytical estimators improve computational efficiency.
Abstract
This paper consider an MMLE (Modified Maximum Likelihood Estimation) based scheme to estimate software reliability using exponential distribution. The MMLE is one of the generalized frameworks of software reliability models of Non Homogeneous Poisson Processes (NHPPs). The MMLE gives analytical estimators rather than an iterative approximation to estimate the parameters. In this paper we proposed SPC (Statistical Process Control) Charts mechanism to determine the software quality using inter failure times data. The Control charts can be used to measure whether the software process is statistically under control or not.
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Taxonomy
TopicsSoftware Reliability and Analysis Research · Fault Detection and Control Systems · Advanced Statistical Process Monitoring