SOFTWARE FAILURE DATA ANALYSIS USING THE LEAST SQUARES APPROACH AND THE TIME PER FAILURE CONCEPT

1995 ◽  
Vol 02 (02) ◽  
pp. 161-176 ◽  
Author(s):  
G.J. KNAFL ◽  
J.A. MORGAN ◽  
R.L. FOLLENWEIDER ◽  
R.M. KARCICH
Keyword(s):  
Software Reliability ◽  
Evaluation Procedure ◽  
Data Set ◽  
Reliability Models ◽  
Mean Time Between Failures ◽  
Software Failure ◽  
Failure Data ◽  
Software Reliability Models ◽  
Power Models ◽  
Failure Intensity

We adapt data analytic techniques to the software reliability setting. We develop an evaluation procedure based on scatterplots of transformed data, crossvalidation using the predicted residual sum of squares (PRESS) criterion, residual plots, and normal plots. We analyze a software failure data set collected at Storage Technology Corporation utilizing this evaluation technique. We identify a new model which, for this data set, outperforms several established software reliability models, including the delayed S-shaped, exponential, inverse linear, logarithmic, power, and log power models. The failure intensity, and hence the reliability, for this model at any point in time is a function of the time per failure, that is, the ratio of cumulative time divided by cumulative failures, a quantity that agrees with the mean time between failures for time points at which failures occur.

FEEDBACK CONTROL SYSTEM FOR SOFTWARE

2007 ◽  
Vol 14 (05) ◽  
pp. 411-429
Author(s):  
NORMAN SCHNEIDEWIND
Keyword(s):  
Feedback Control ◽  
Control Systems ◽  
Software Reliability ◽  
Space Shuttle ◽  
Physical Systems ◽  
Reliability Models ◽  
Software Failure ◽  
Failure Data ◽  
Software Reliability Models ◽  
Feedback Control Systems

Feedback control systems are used in many walks of life, including automobiles, airplanes, and nuclear reactors. These are all physical systems, albeit with a considerable does of software. It occurred to us that there is no reason that feedback control systems could not be applied to the software process, specifically dealing with reliability analysis, test, and prediction. Thus, we constructed a model of such a system and analyzed whether feedback control, in the form of error signals representing deviations from desired behavior, could bring observed behavior in conformance with specifications. To conduct the experiment, we used NASA Space Shuttle software failure data and analyzed the feedback when no faults were removed versus removing faults. In making this evaluation two software reliability models were used: the Musa Logarithmic Model and the Schneidewind Model. In general, feedback based on fault removal allowed the software reliability process to provide more accurate predictions and, hence, finer control over the process.

IMPROVING SOFTWARE RELIABILITY MODELING USING MACHINE LEARNING TECHNIQUES

2008 ◽  
Vol 18 (07) ◽  
pp. 965-986 ◽  
Author(s):  
FENGZHONG ZOU ◽  
JOSEPH DAVIS
Keyword(s):  
Machine Learning ◽  
Software Reliability ◽  
Machine Learning Techniques ◽  
Data Noise ◽  
Reliability Models ◽  
Software Failure ◽  
Failure Data ◽  
Software Reliability Models ◽  
Learning Techniques ◽  
Standard Software

It is well-known that software failure data often contain noise, making the reliability estimation problematic. In particular, the kind of data noise inherent in software failure data is biased. There is no upper bound for the value of a noisy data point, but there is a lower bound of zero. This may lead to over-optimistic estimation of the reliability when using maximum likelihood or least square methods based on standard software reliability models. We attempt to address this problem by modeling software failure data using machine learning techniques such as support vector machine regression and generalized additive models, which have mechanisms that are capable of dealing with data noise. We then analyse the results from machine learning modeling, and compare them to that of some generalized linear modeling techniques that are equivalent to standard software reliability models. The validity of the machine learning modeling of noisy software failure data is evaluated through this comparison.

A SOFTWARE RELIABILITY MODEL FOR ARTIFICIAL INTELLIGENCE PROGRAMS

1993 ◽  
Vol 03 (01) ◽  
pp. 99-114 ◽  
Author(s):  
FAROKH B. BASTANI ◽  
ING-RAY CHEN ◽  
TA-WEI TSAO
Keyword(s):  
Artificial Intelligence ◽  
Software Reliability ◽  
Planning Problem ◽  
Reliability Growth ◽  
Reliability Model ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Correctness Criterion

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

scholarly journals A Software Reliability Model Using Quantile Function

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Bijamma Thomas ◽  
Midhu Narayanan Nellikkattu ◽  
Sankaran Godan Paduthol
Keyword(s):  
Software Reliability ◽  
Real Data ◽  
Quantile Function ◽  
Data Set ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Reliability Characteristics ◽  
Proposed Model ◽  
L Moments

We study a class of software reliability models using quantile function. Various distributional properties of the class of distributions are studied. We also discuss the reliability characteristics of the class of distributions. Inference procedures on parameters of the model based on L-moments are studied. We apply the proposed model to a real data set.

scholarly journals Application of EM Algorithm to NHPP-Based Software Reliability Assessment with Generalized Failure Count Data

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 985
Author(s):  
Hiroyuki Okamura ◽  
Tadashi Dohi
Keyword(s):  
Em Algorithm ◽  
Software Reliability ◽  
Count Data ◽  
Failure Time ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Time Data ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models

Software reliability models (SRMs) are widely used for quantitative evaluation of software reliability by estimating model parameters from failure data observed in the testing phase. In particular, non-homogeneous Poisson process (NHPP)-based SRMs are the most popular because of their mathematical tractability. In this paper, we focus on the parameter estimation algorithm for NHPP-based SRMs and discuss the EM algorithm for generalized fault count data. The presented algorithm can be applied for failure time data, failure count data, and their mixture. The paper derives the EM-step formulas for basic 12 NHPP-based SRMs and demonstrate a numerical experiment to present the convergence property of our algorithms. The developed algorithms are suitable for an automatic tool for software reliability evaluation.

BOOTSTRAP CONFIDENCE INTERVALS OF SOFTWARE RELIABILITY MEASURES BASED ON A GAMMA FUNCTION MODEL

2008 ◽  
Vol 15 (01) ◽  
pp. 55-66
Author(s):  
MITSUHIRO KIMURA
Keyword(s):  
Confidence Intervals ◽  
Gamma Function ◽  
Software Reliability ◽  
Reliability Assessment ◽  
Numerical Differentiation ◽  
Model Parameters ◽  
Function Model ◽  
Data Set ◽  
Reliability Models ◽  
Software Reliability Models

This paper focuses on the generalization of several software reliability models and the derivation of confidence intervals of reliability assessment measures. First we propose a gamma function model as a generalized model, and discuss how to obtain the confidence intervals from a data set by using a bootstrap scheme when the size of the data set is small. A two-parameter numerical differentiation method is applied to the data set to estimate the model parameters. We also show several numerical illustrations of software reliability assessment.

SOFTWARE RELIABILITY MODELS WITH TWO DEBUGGING RATES

1999 ◽  
Vol 06 (01) ◽  
pp. 31-42 ◽  
Author(s):  
OLIVIER GAUDOIN
Keyword(s):  
Maximum Likelihood ◽  
Simple Model ◽  
Software Reliability ◽  
Failure Process ◽  
Real Data ◽  
Software Faults ◽  
Reliability Models ◽  
Software Failure ◽  
New Class ◽  
Software Reliability Models

A new class of software reliability models is proposed, based on practical considerations about the failure process and the influence of debugging on the successive software failure rates. It is assumed that, at each correction, a part of the previous software faults are removed and new faults can be introduced. Several well-known models belong to this class. With some additional assumptions, a simple model is proposed. The maximum likelihood equations for the estimation of its parameters are derived. The model is applied on real data and is compared to usual software reliability models.

An NHPP Software Reliability Model and Its Comparison

1997 ◽  
Vol 04 (03) ◽  
pp. 269-282 ◽  
Author(s):  
Hoang Pham ◽  
Xuemei Zhang
Keyword(s):  
Software Reliability ◽  
Visual Basic ◽  
Model Parameters ◽  
Data Sets ◽  
New Model ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Better Than

In this paper, software reliability models based on a nonhomogeneous Poisson process (NHPP) are summarized. A new model based on NHPP is presented. All models are applied to two widely used data sets. It can be shown that for the failure data used here, the new model fits and predicts much better than the existing models. A software program is written, using Excel & Visual Basic, which can be used to facilitate the task of obtaining the estimators of model parameters.

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