ML ESTIMATES FOR CROW/AMSAA RELIABILITY GROWTH MODEL FOR GROUPED AND MIXED TYPES OF SOFTWARE FAILURE DATA

2004 ◽  
Vol 11 (04) ◽  
pp. 329-337
Author(s):  
RANI ◽  
R. B. MISRA
Keyword(s):  
Growth Models ◽  
Quantitative Measure ◽  
Interval Data ◽  
Comprehensive Treatment ◽  
Reliability Growth ◽  
Data Types ◽  
Software Failure ◽  
Failure Data ◽  
Stochastic Basis ◽  
Hybrid Data

A number of software reliability growth models have been proposed into the literature for estimating reliability during software testing. Duane's model,7 originally proposed for hardware reliability is also used in estimating reliability of the software during development testing. Graphical interpretation of Duane's postulate subsequently was given a concrete stochastic basis by Crow,3 and provided a comprehensive treatment of this model in the context of reliability growth and demonstrated its elegant inferential aspects. Parameters of the Crow model have physical interpretation and can yield quantitative measure for reliability growth assessment. This paper proposes a simple and efficient procedure to determine parameters of Crow/AMSAA model using one dimensional bisection method for grouped/interval data, where failures are recorded at various time points. In addition this paper proposes a method to estimate parameters when there exist a mixture of grouped and individual (mixed or hybrid) data types. Proposed method's application is illustrated with numerical examples using both simulated and real software failure data.

Predicting software reliability

1989 ◽  
Vol 327 (1596) ◽  
pp. 513-527 ◽  
Keyword(s):  
Conceptual Model ◽  
Software Reliability ◽  
Predictive Accuracy ◽  
Growth Models ◽  
High Reliability ◽  
A Priori ◽  
Failure Process ◽  
Reliability Growth ◽  
Critical Systems ◽  
Software Failure

This paper surveys some aspects of the state of the art of software reliability modelling. By far the greatest effort to date has been expended on the problem of assessing and predicting the reliability growth which takes place as faults are found and fixed, so the greater part of the paper addresses this problem. We begin with a simple conceptual model of the software failure process in order to set the scene and motivate the detailed stochastic models which follow. This conceptual model suggests certain minimal characteristics which all growth models for software should possess. There are now several detailed models which aim to represent software reliability growth, but their accuracy of prediction seems to vary greatly from one application to another. As it is not possible to decide a priori which will give the most accurate answers for a particular context, the potential user is faced with a dilemma. There seems to be no alternative to analysing the predictive accuracy on the data source under examination and selecting for the current prediction that model which has demonstrated greatest accuracy on earlier predictions for that data. Some ways in which this selection can be effected are described in the paper. It turns out that examination of accuracy of past predictions can be used to improve future predictions by a simple recalibration procedure. Sometimes this technique works dramatically well, and results are shown for some real software failure data. Finally, there is a brief discussion of some wider issues which are not covered by a simple reliability growth study. These include cost modelling, the evaluation of software engineering methodologies, the relationship between testing and reliability, and the important issues of ultra-high reliability and safety-critical systems. On the last point, a warning note is sounded on the wisdom of building systems which depend on software having a very high reliability; this will be very hard to achieve and even harder to demonstrate.

scholarly journals System Reliability Growth Analysis during Warranty

2019 ◽  
Vol 4 (1) ◽  
pp. 85-94 ◽  
Author(s):  
James Li ◽  
Greg Collins ◽  
Ravi Govindarajulu
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Growth Models ◽  
Primary Objective ◽  
Reliability Growth ◽  
Failure Data ◽  
Duane Model ◽  
Actual Field ◽  
Reliability Performance ◽  
Field Failure

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

scholarly journals Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Subburaj Ramasamy ◽  
Indhurani Lakshmanan
Keyword(s):  
Software Reliability ◽  
Learning Approach ◽  
Reliability Growth ◽  
Software Failure ◽  
The Past ◽  
Failure Data ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Software Reliability Growth ◽  
Effort Function

Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.

An Adaptive EM Algorithm for the Maximum Likelihood Estimation of Non-Homogeneous Poisson Process Software Reliability Growth Models

2017 ◽  
Vol 24 (04) ◽  
pp. 1750020 ◽  
Author(s):  
Vidhyashree Nagaraju ◽  
Lance Fiondella ◽  
Panlop Zeephongsekul ◽  
Thierry Wandji
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Homogeneous Poisson Process ◽  
Em Algorithms ◽  
Failure Data ◽  
Software Reliability Growth ◽  
Non Homogeneous Poisson Process

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM a ) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease. This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria. a Acronyms are not pluralized.

DEBUGGING PROCESS-ORIENTED DISCRETE SOFTWARE RELIABILITY MODELING

2009 ◽  
Vol 16 (04) ◽  
pp. 357-370 ◽  
Author(s):  
SHINJI INOUE ◽  
NAOKI IWAMOTO ◽  
SHIGERU YAMADA
Keyword(s):  
Discrete Time ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Reliability Growth ◽  
Reliability Modeling ◽  
New Approach ◽  
Time Modeling ◽  
Software Reliability Growth ◽  
Discrete Time Modeling

This paper discusses an new approach for discrete-time software reliability growth modeling based on an discrete-time infinite server queueing model, which describes a debugging process in a testing phase. Our approach enables us to develop discrete-time software reliability growth models (SRGMs) which could not be developed under conventional discrete-time modeling approaches. This paper also discuss goodness-of-fit comparisons of our discrete-time SRGMs with conventional continuous-time SRGMs in terms of the criterion of the mean squared errors, and show numerical examples for software reliability analysis of our models by using actual data.

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