scholarly journals Phase-Type Modeling Approaches for Software Reliability Modeling with Debugging Process

2019 ◽  
Vol 5 (1) ◽  
pp. 76-82
Author(s):  
Shinji Inoue ◽  
Shigeru Yamada
Keyword(s):  
Software Reliability ◽  
Data Sets ◽  
Probability Models ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Modeling Approach ◽  
Software Reliability Models ◽  
Phase Type ◽  
Few Data ◽  
Reliability Measurement

Reflecting the software fault debugging procedure or environment of testing activities on software reliability models is often discussed as the approaches for improving assessment accuracy for model-based reliability assessment. We discuss a modeling approach reflecting software debugging procedure based on phase-type modeling scheme and propose probability models for software reliability measurement. Further, we give brief consideration for the usefulness of this modeling approach by using a few data sets.

Unification of Software Reliability Models by Self-Exciting Point Processes

1997 ◽  
Vol 29 (2) ◽  
pp. 337-352 ◽  
Author(s):  
Yiping Chen ◽  
Nozer D. Singpurwalla
Keyword(s):  
Software Reliability ◽  
Point Processes ◽  
Computer Software ◽  
Probability Models ◽  
Reliability Models ◽  
Common Structure ◽  
Software Reliability Models ◽  
Special Cases ◽  
Active Debate ◽  
The Subject

Assessing the reliability of computer software has been an active area of research in computer science for the past twenty years. To date, well over a hundred probability models for software reliability have been proposed. These models have been motivated by seemingly unrelated arguments and have been the subject of active debate and discussion. In the meantime, the search for an ideal model continues to be pursued. The purpose of this paper is to point out that practically all the proposed models for software reliability are special cases of self-exciting point processes. This perspective unifies the very diverse approaches to modeling reliability growth and provides a common structure under which problems of software reliability can be discussed.

Test Environment-Dependent Discrete Binomial-Type Software Reliability Models

2017 ◽  
Vol 24 (01) ◽  
pp. 1750003 ◽  
Author(s):  
Shinji Inoue ◽  
Shigeru Yamada
Keyword(s):  
Software Reliability ◽  
Information Criterion ◽  
Test Environment ◽  
Environment Factors ◽  
Reliability Models ◽  
Testing Phase ◽  
Modeling Approach ◽  
Software Reliability Models ◽  
Binomial Type ◽  
Mean Square Errors

We have no doubt that the software reliability growth process in the testing phase depends on the test environment factors, such as the attained testing coverage, the number of test-runs and the debugging skills, which affect the software failure occurrence or fault detection phenomenon in the testing phase. In this paper, we propose software reliability models that consider the effects of the testing environment factors. Our models are developed by a program size-dependent discrete binomial-type software reliability modeling approach. This modeling approach is also consistent with software reliability data collection and enables us to consider the effect of the program size. Finally, we compare the accuracy of our models in terms of mean square errors (MSE) and Akaike’s information criterion (AIC) with the existing corresponding model by using actual data.

SOFTWARE RELIABILITY MODELING BASED ON MIXED POISSON DISTRIBUTIONS

2008 ◽  
Vol 15 (01) ◽  
pp. 19-32 ◽  
Author(s):  
HIROYUKI OKAMURA ◽  
TADASHI DOHI
Keyword(s):  
Poisson Process ◽  
Software Reliability ◽  
Estimation Method ◽  
Modeling Framework ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Mixed Poisson Process ◽  
The Difference ◽  
Non Homogeneous Poisson Process

This paper considers a novel modeling framework of software reliability models (SRMs). The proposed SRMs are based on the mixed Poisson distribution (MPD), which can involve the non-homogeneous Poisson process (NHPP) based SRMs completely, but are not always equivalent to them. More precisely, the MPD-based SRMs provide a mixture of NHPPs, and their statistical properties follows the mixed Poisson process. We develop a parameter estimation method for the MPD-based SRMs based on EM algorithm. In numerical examples, we mainly investigate the difference between conventional NHPP-based SRMs and MPD-based SRMs in the viewpoints of estimating parameters and software reliability.

Unification of Software Reliability Models by Self-Exciting Point Processes

1997 ◽  
Vol 29 (02) ◽  
pp. 337-352 ◽  
Author(s):  
Yiping Chen ◽  
Nozer D. Singpurwalla
Keyword(s):  
Software Reliability ◽  
Point Processes ◽  
Computer Software ◽  
Probability Models ◽  
Reliability Models ◽  
Common Structure ◽  
Software Reliability Models ◽  
Special Cases ◽  
Active Debate ◽  
The Subject

Assessing the reliability of computer software has been an active area of research in computer science for the past twenty years. To date, well over a hundred probability models for software reliability have been proposed. These models have been motivated by seemingly unrelated arguments and have been the subject of active debate and discussion. In the meantime, the search for an ideal model continues to be pursued. The purpose of this paper is to point out that practically all the proposed models for software reliability are special cases of self-exciting point processes. This perspective unifies the very diverse approaches to modeling reliability growth and provides a common structure under which problems of software reliability can be discussed.

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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