scholarly journals Reliability assessment and sensitivity analysis of software reliability growth modeling based on software module structure

2005 ◽  
Vol 76 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Jung-Hua Lo ◽  
Chin-Yu Huang ◽  
Ing-Yi Chen ◽  
Sy-Yen Kuo ◽  
Michael R. Lyu
Keyword(s):  
Sensitivity Analysis ◽  
Software Reliability ◽  
Reliability Assessment ◽  
Growth Modeling ◽  
Module Structure ◽  
Reliability Growth ◽  
Software Module ◽  
Software Reliability Growth

A COMPONENT-ORIENTED RELIABILITY ASSESSMENT METHOD FOR OPEN SOURCE SOFTWARE

2008 ◽  
Vol 15 (01) ◽  
pp. 33-53 ◽  
Author(s):  
YOSHINOBU TAMURA ◽  
SHIGERU YAMADA
Keyword(s):  
Neural Network ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Reliability Assessment ◽  
Growth Modeling ◽  
Reliability Growth ◽  
Entire System ◽  
Development Environment ◽  
Software Reliability Growth

Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, an OSS (open source software) system which serves as key components of critical infrastructures in the society is still ever-expanding now. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for the OSS, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system, we propose a new approach to user-oriented software reliability assessment by creating a fusion of neural network and software reliability growth modeling. In this paper, we show application examples of component-oriented software reliability assessment based on neural network and software reliability growth modeling for the OSS. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the OSS. Moreover, we develop the testing management tool for OSS.

EXTENDED HAZARD RATE MODELS FOR SOFTWARE RELIABILITY ASSESSMENT WITH EFFECT AT CHANGE-POINT

2013 ◽  
Vol 20 (02) ◽  
pp. 1350009 ◽  
Author(s):  
SHINJI INOUE ◽  
SHIHO HAYASHIDA ◽  
SHIGERU YAMADA
Keyword(s):  
Software Reliability ◽  
Hazard Rate ◽  
Reliability Assessment ◽  
Reliability Growth ◽  
Testing Environment ◽  
Software Failure ◽  
Testing Phase ◽  
Hazard Rate Model ◽  
Hazard Rate Models ◽  
Software Reliability Growth

A software hazard rate model is known as one of the important and useful mathematical models for describing the software failure occurrence phenomenon observed in a testing phase. It is difficult to say that the testing environment always constant during a testing phase due to changing the specification and fault target and so forth. Therefore, taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment. In this paper, we develop extended software hazard rate models based on well-known Jelinski–Moranda and Moranda models, by considering with a change of testing environment. Especially in this paper, we incorporate the uncertainty of the effect of the change on the software reliability growth process into the software hazard rate modeling. Finally, we show numerical examples for our models and results of model comparisons by using actual data.

SOFTWARE RELIABILITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORK BASED FLEXIBLE MODEL INCORPORATING FAULTS OF DIFFERENT COMPLEXITY

2008 ◽  
Vol 15 (02) ◽  
pp. 113-127 ◽  
Author(s):  
P. K. KAPUR ◽  
SUNIL K. KHATRI ◽  
MASHAALLAH BASIRZADEH
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Assessment ◽  
Reliability Growth ◽  
Removal Process ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Fault Removal ◽  
Artificial Neural

With growth in demand for zero defects, predicting reliability of software products is gaining importance. Software Reliability Growth Models (SRGM) are used to estimate the reliability of a software product. We have a large number of SRGM; however none of them works across different environments. Recently, Artificial Neural Networks have been applied in software reliability assessment and software reliability growth prediction. In most of the existing research available in the literature, it is considered that similar testing effort is required on each debugging effort. However, in practice, different amount of testing efforts may be required for detection and removal of different type of faults on basis of their complexity. Consequently, faults are classified into three categories on basis of complexity: simple, hard and complex. In this paper we apply neural network methods to build software reliability growth models (SRGM) considering faults of different complexity. Logistic learning function accounting for the expertise gained by the testing team is used for modeling the proposed model. The proposed model assumes that in the simple faults the growth in removal process is uniform whereas, for hard and complex faults, removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The proposed model has been validated, evaluated and compared with other NHPP model by applying it on two failure/fault removal data sets cited from real software development projects. The results show that the proposed model with logistic function provides improved goodness-of-fit for software failure/fault removal data.

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