Entropy Based Software Reliability Analysis of Multi-Version Open Source Software

Singh, Vir; Sharma, Meera; Pham, Hoang

doi:10.1109/tse.2017.2766070

Cited by 48 publications

(30 citation statements)

References 41 publications

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“…A testing effort and multivariate function based fault prediction rate has been proposed for SRGM [19]. Recently, entropy based SRGMs were proposed to measure the software reliability growth and these models were further used to predict software release time [23,34]. In another studies [4,25] remaining software faults from previous release and current faults based multirelease SRGM has been proposed.…”

Section: Related Workmentioning

confidence: 99%

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Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

et al. 2020

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During Open Source Software (OSS) development, users submit "new features (NFs)", "feature improvements (IMPs)" and bugs to fix. A proportion of these issues get fixed before the next software release. During the introduction of NFs and IMPs, the source code files change. A proportion of these source code changes may result in generation of bugs. We have developed calendar time and entropy-dependent mathematical models to represent the growth of OSS based on the rate at which NFs are added, IMPs are added, and bugs introduction rate.The empirical validation has been conducted on five products, namely "Avro, Pig, Hive, jUDDI and Whirr" of the Apache open source project. We compared the proposed models with eminent reliability growth models, Goel and Okumoto (1979) and Yamada et al. (1983) and found that the proposed models exhibit better goodness of fit.

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Section: Related Workmentioning

confidence: 99%

“…Tab. 2 shows the versions for which we have taken the cumulative data and renamed as different release numbers [23].…”

Section: Description Of Datasetsmentioning

confidence: 99%

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Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

et al. 2020

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“…In the case of the big data, it is very difficult to estimate the upper and lower limits from the number of big data in the stochastic models, because the volume of data is huge. Many methods of OSS empirical assessment have been proposed [4,5,14,15]. However, the size of fault data in OSS is large.…”

Section: Multiple Regression Analysismentioning

confidence: 99%

Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

Tamura

Yamada

2020

MAKE

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Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

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“…In thestudy, it was observed that the potential complexity of code change(entropy)-based approach wasbetter. Instudy [ 59 ], the authors proposed entropy-based software reliability analysis. The experimental result was validated on five products of the Apache open source projects, namely Avro, Pig, Hive, jUDDI and Whirr.…”

Section: Related Workmentioning

confidence: 99%

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Kumari¹,

Misra

et al. 2019

Entropy

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A software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.

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Entropy Based Software Reliability Analysis of Multi-Version Open Source Software

Cited by 48 publications

References 41 publications

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

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