Amrit L. Goel

Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP). The failure process is analyzed to develop a suitable meanvalue function for the NHPP; expressions are given for several performance measures. Actual software failure data are analyzed and compared with a previous analysis.

Software Reliability Models: Assumptions, Limitations, and Applicability

A number of analytical models have been proposed during the past 15 years for assessing the reliability of a software system. In this paper we present an overview of the key modeling approaches, provide a critical analysis of the underlying assumptions, and assess the limitations and applicability of these models during the software development cycle. We also propose a step-by-step procedure for fitting a model and illustrate it via an analysis of failure data from a medium-sized real-time command and control software system.

Optimum release time for software systems based on reliability and cost criteria

The problem of determining the optimum time when testing can stop and the system can be considered ready for operational use is considered. This decision, of course, depends on the model for the software failure phenomenon and the criterion used for evaluating system readiness. With the time dependent error detection rate model of Goel and Okumoto, two criteria are investigated: software reliability and total expected cost. Based on the cost criterion, an optimum release policy is derived and its sensitivity to the model parameters is studied. The results are illustrated by numerical examples.

Empirical data modeling in software engineering using radial basis functions

Many empirical studies in software engineering involve relationships between various process and product characteristics derived via linear regression analysis. We propose an alternative modeling approach using radial basis functions (RBFs) which provide a flexible way to generalize linear regression function. Further, RBF models possess strong mathematical properties of universal and best approximation. We present an objective modeling methodology for determining model parameters using our recent SG algorithm, followed by a model selection procedure based on generalization ability. Finally, we describe a detailed RBF modeling study for software effort estimation using a well-known NASA dataset.

Determination of A.R.L. and a Contour Nomogram for Cusum Charts to Control Normal Mean

The Average Run Length of a Cusum chart for controlling a normal mean is calculated by solving the systems of linear equations which approximate the integral equations for the required quantities. The accuracy of approximation by this method is numerically evaluated and the results are compared with those obtained by other approximate methods. The construction and use of a new nomogram based on the contours of Average Run Lengths La . and Lr drawn in the h√n/σ—|μ – k|√n/σ plane is discussed. Numerical examples are given to illustrate the flexibility and convenience provided by this nomogram in the design of Cusum charts.

Software error detection model with applications

This paper deals with the modeling of software errors encountered in a small and a large software system. A deterministic analysis of software failure process is presented to obtain an appropriate mean value function for a nonhomogeneous Poisson process. Several quantitative measures for software quality assessment are also proposed. Statistical techniques of inference about unknown parameters are discussed and detailed analyses of software error data from two systems are presented.

Models for Hardware-Software System Operational-Performance Evaluation

Stochastic models for hardware-software systems are developed and used to study their performance as a function of hardware-software failure and maintenance rates. Expressions are derived for the distribution of time to a specified number of software errors, system occupancy probabilities, system reliability, availability, and average availability. The behavior of these measures is investigated via numerical examples.

Gaussian radial basis functions for simulation metamodeling

The paper presents a novel approach for developing simulation metamodels using Gaussian radial basis functions. This approach is based on some recently developed mathematical results for radial basis functions. It is systematic, explicitly controls the underfitting and overfitting tradeoff, and uses a fast computational algorithm that requires minimal human involvement. This approach is illustrated by developing metamodels for the M/M/1 queueing system.

When to stop testing and start using software

During the last decade, numerous studies have been undertaken to quantify the failure process of large scale software systems. (see for example, references 1-12.) An important objective of these studies is to predict software performance and use the information for decision making. An important decision of practical concern is the determination of the amount of time that should be spent in testing. This decision of course will depend on the model used for describing the failure phenomenon and the criterion used for determining system readiness. In this paper we present a cost model based on the time dependent fault detection rate model of Goel and Okumoto (4,5) and describe a policy that yields the optimal value of test time T.

A Note on Availability for a Finite Interval

A definition of availability having a probabilistic guarantee for a finite interval is made. Examples and asymptotic solution are presented.