Using Software Reliability Growth Models in Practice

Almering, V.; Genuchten, Michiel van; Cloudt, G.; Sonnemans, P.J.M.

doi:10.1109/ms.2007.182

Cited by 79 publications

(36 citation statements)

References 14 publications

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“…Related work such as Almering et al [1], Goel et al [3] and Yamada et al [14] all investigate when a software is ready to be used. These works use the Software Reliability Growth Model (SRGM) of the software evolution process to grasp the process of the convergence of defects discovered in software as the 'growth curve of the S-Shape (Sigmond curve)'.…”

Section: Motivationmentioning

confidence: 99%

Understanding When to Adopt a Library: A Case Study on ASF Projects

Ihara

Fujibayashi

Suwa

et al. 2017

IFIP Advances in Information and Communication Technology

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Abstract. Software libraries are widely used by both industrial and open source client projects. Ideally, a client user of a library should adopt the latest version that the library project releases. However, sometimes the latest version is not better than a previous version. This is because the latest version may include additional developer effort to test and integrate all changed features. In this study, our main goal is to better understand the relationship between adoption of library versions and its release cycle. Specifically, we conducted an empirical study of release cycles for 23 libraries and how they were adopted by 415 Apache Software Foundation (ASF) client projects. Our findings show that software projects are quicker to update earlier rapid-release libraries compared to library projects with a longer release cycle. Moreover, results suggest that software projects are more likely to adopt the latest version of a rapid-release library compared to libraries with a longer release cycles.

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Section: Motivationmentioning

confidence: 99%

Understanding When to Adopt a Library: A Case Study on ASF Projects

Ihara

Fujibayashi

Suwa

et al. 2017

IFIP Advances in Information and Communication Technology

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“…It can describe and predict the evolution/growth of a quantitative measure over time. In software engineering, this curve has been applied primarily in the context of software reliability (e.g., [19], [20], [21]). …”

Section: B Visualization Techniques and Representationsmentioning

confidence: 99%

Visualizing the Evolution of Systems and Their Library Dependencies

Kula

Roover

Germán

et al. 2014

2014 Second IEEE Working Conference on Software Visualization

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Abstract-System maintainers face several challenges stemming from a system and its library dependencies evolving separately. Novice maintainers may lack the historical knowledge required to efficiently manage an inherited system. While some libraries are regularly updated, some systems keep a dependency on older versions. On the other hand, maintainers may be unaware that other systems have settled on a different version of a library. In this paper, we visualize how the dependency relation between a system and its dependencies evolves from two perspectives. Our system-centric dependency plots (SDP) visualize the successive library versions a system depends on over time. The radial layout and heat-map metaphor provide visual clues about the change in dependencies along the system's release history. From this perspective, maintainers can navigate to a library-centric dependants diffusion plot (LDP). The LDP is a time-series visualization that shows the diffusion of users across the different versions of a library. We demonstrate on real-world systems how maintainers can benefit from our visualizations through four case scenarios.

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“…Therefore, the binomial distribution is usually applied to the probability mass function of the initial fault content especially for the middle size of software and the Poisson distribution can be applied to N 0 of the large size of software. The practical applicability of SRGMs in which the initial fault content follows the Poisson distribution has been discussed in the literatures (Almering et al 2007;Ohba 1984;Pham 2000). The references Ohba (1984) and Pham (2000) discussed software reliability growth modeling in which the initial fault content follows the Poisson distribution and the binomial distribution theoretically.…”

Section: Introductionmentioning

confidence: 99%

“…The references Ohba (1984) and Pham (2000) discussed software reliability growth modeling in which the initial fault content follows the Poisson distribution and the binomial distribution theoretically. The reference Almering et al (2007) addressed the practical applications of the software reliability growth models mentioned above by using actual software reliability data. Therefore, we can see that SRGMs in which the initial fault content follows the Poisson distribution has been widely applied in practical software reliability assessment due to high applicability of NHPP models in terms of the fitting and predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor

2015

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We observe a phenomenon that the probabilistic characteristic of software failureoccurrence time-interval change notably in an actual testing-phase of a software development process. Testing-time observing such phenomenon is ordinarily called change-point. This phenomenon is treated as one of the factors affecting the accuracy of software reliability assessment based on a software reliability growth model. And regarding software reliability growth modeling, a bivariate software reliability growth model, which describes a software reliability growth process depending on the software reliability growth factor consists of testing-time and testing-effort factors, has been proposed for improving software reliability assessment accuracy based on the software reliability growth process. In this paper, we develop a bivariate software reliability growth model considering with the uncertainty of the change of software failure-occurrence phenomenon at the change-point for improving more the accuracy. Finally we show numerical examples of our model by using actual data.

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Using Software Reliability Growth Models in Practice

Abstract: Using software reliability growth models in practiceAlmering, V.A.; van Genuchten, M.J.I.M.; Sonnemans, P.J.M.; Cloudt, G.

Cited by 79 publications

References 14 publications

Understanding When to Adopt a Library: A Case Study on ASF Projects

Understanding When to Adopt a Library: A Case Study on ASF Projects

Visualizing the Evolution of Systems and Their Library Dependencies

Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor

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