Multi Release Reliability Growth Modeling for Open Source Software Under Imperfect Debugging

2018 ◽  
pp. 77-86
Author(s):  
Diwakar ◽  
Anu G. Aggarwal
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Growth Modeling ◽  
Reliability Growth ◽  
Imperfect Debugging

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.

Generalized Multi-Release Framework for Fault Prediction in Open Source Software

2019 ◽  
Vol 7 (4) ◽  
pp. 86-107 ◽  
Author(s):  
Shozab Khurshid ◽  
A.K. Shrivastava ◽  
Javaid Iqbal
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Change Point ◽  
General Framework ◽  
Fault Prediction ◽  
Change Points ◽  
Software Project ◽  
Imperfect Debugging ◽  
Developing Communities ◽  
Quality And Reliability

Software developing communities are shifting to open source software (OSS) because of the reason that software development takes place in successive releases, thereby improving its quality and reliability. Multi-release development of OSS can provide an opportunity to inculcate the dynamic needs of the user in a very short span of time to survive in the market. In spite of having these benefits, numerous challenges can be faced during the multi-release OSS development. Some of the challenges can be the generation of errors during the addition of new features. To address the changing fault detection process, a change point phenomenon is considered so as to give more practicality to the model. In this article, we present a general framework for multi-release OSS modelling incorporating imperfect debugging and change points. Parameter estimation and model validation is done on the three releases of Apache, an open source software project.

Fault Prediction Modelling in Open Source Software Under Imperfect Debugging and Change-Point

2021 ◽  
pp. 277-293
Author(s):  
Shozab Khurshid ◽  
A. K. Shrivastava ◽  
Javaid Iqbal
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Change Point ◽  
General Framework ◽  
Removal Rate ◽  
Fault Prediction ◽  
Imperfect Debugging ◽  
Fault Removal ◽  
Distance Approach ◽  
Prediction Capability

Instant demand of products and services by technologically active users has increased the demand for open source software (OSS)-based applications. Unfortunately, with the complexity and lack of understanding of OSS-based systems, it becomes difficult for a testing team to remove the faults and the fault removal rate becomes low in comparison to what it should be. This also results in generating new faults during removal. Also, the rate at which the testing team detects/corrects fault need not be same during the entire process of testing due to various reasons viz. change in testing strategy, understanding of code, change in resources, etc. In the existing literature on OSS, authors have developed many models considering the above aspects separately. In this article, all of the above aspects have been combined to develop a general framework for predicting the number of faults in OSS. The comparison of eight models on the basis of their prediction capability on two well-known Open Source Software datasets is created and then ranked using normalized criteria distance approach.

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