TESTING-DOMAIN DEPENDENT SOFTWARE RELIABILITY GROWTH MODELS AND THEIR COMPARISONS OF GOODNESS-OF-FIT

2001 ◽  
Vol 08 (03) ◽  
pp. 205-218 ◽  
Author(s):  
SHIGERU YAMADA ◽  
TAKAJI FUJIWARA
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Test Case ◽  
Test Cases ◽  
Software System ◽  
Reliability Growth ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

A software developer has to test to verify the implemented functions based on its requirement specification. We use many various test-cases for testing. Then, there is a set of the modules and functions in the software system to be influenced by the executed test-cases. The set is called a testing-domain and it spreads with the progress of testing. The growth rate of testing-domain in the software system is closely related to the quality and quantity of the executed test-cases by testing. Further, the quality of test-cases is related to the testing-skill of test-case designers. In this paper, we discuss testing-domain dependent software reliability growth models. The models are formulated by a nonhomogeneous Poisson process. Further, we propose three kinds of testing-domain, i.e., the basic testing-domain, the testing-domain with skill-factor, and the testing-domain with imperfect debugging. Finally, these models are applied to fault data observed in actual development projects, the software reliability analysis results are shown, and the comparisons of goodness-of-fit with the conventional software reliability growth models are performed.

scholarly journals Testing Domain Dependent Software Reliability Growth Models

2017 ◽  
Vol 2 (3) ◽  
pp. 140-149 ◽  
Author(s):  
Deepika ◽  
Ompal Singh ◽  
Adarsh Anand ◽  
Jyotish N. P. Singh
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Test Cases ◽  
Data Sets ◽  
Reliability Growth ◽  
Domain Specific ◽  
Software Reliability Growth Models ◽  
Domain Specific Knowledge ◽  
Software Reliability Growth ◽  
Role Based

Software Reliability Growth Models (SRGMs) are supporting software industries in expecting and scrutinizing quality of software. Numerous SRGMs have been proposed; majority of which concentrate on testing period of software. For testing, domain specific knowledge plays a very crucial role. Based on necessity condition, a set of programmes are in testing phase of software development. “Domain testing is a software technique in which small number of test cases is selected for trial. These sets of testing paths, all of which are to be eventually influenced by designed test cases are called the testing domain which expands with the progress of testing”. Keeping this concept in mind, we propose SRGMs with the concept of testing domain with exponential coverage. Utility of proposed framework has been emphasized in this paper through some models pertaining to different distribution i.e Exponential, Logistic, Weibull and Rayleigh. Moreover, the data analysis is performed to find the estimates of parameters by fitting the models on authentic data sets.

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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