Reliability Modeling of OSS Systems based on Innovation-Diffusion Theory and Imperfect Debugging

Gandhi, Neha; Gondwal, Neha; Tandon, Abhishek

doi:10.15439/2017km48

Cited by 3 publications

(8 citation statements)

References 17 publications

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“…• Perfect debugging methodology [25]: is the detection process of the faults without incorporating additional faults. This methodology contains only one case.…”

mentioning

confidence: 99%

“…Case 1 [25]: the function of fault content represented by α(N), which equals constant a. This case means debugging process will not cause new faults.…”

mentioning

confidence: 99%

“…• Imperfect debugging methodology [25]: is the possibility of introducing new faults while fixing the previous one. This methodology contains four cases.…”

mentioning

confidence: 99%

“…Case 2 [25]: the number of faults is a linear function of the user's number as the following equation:…”

mentioning

confidence: 99%

“…Case 3 [25]: the faults' number introduced exponentially related to the number of users according to the following equation:…”

mentioning

confidence: 99%

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Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Ramadan¹,

Harb²,

Mousa³

et al. 2022

IJITCS

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Nowadays, computer software plays a significant role in all fields of our life. Essentially open-source software provides economic benefits for software companies such that it allows building new software without the need to create it from scratch. Therefore, it is extremely used, and accordingly, open-source software’s quality is a critical issue and one of the top research directions in the literature. In the development cycles of the software, checking the software reliability is an important indicator to release software or not. The deterministic and probabilistic models are the two main categories of models used to assess software reliability. In this paper, we perform a comparative study between eight different software reliability models: two deterministic models, and six probabilistic models based on three different methodologies: perfect debugging, imperfect debugging, and Gompertz distribution. We evaluate the employed models using three versions of a standard open-source dataset which is GNU’s Not Unix Network Object Model Environment projects. We evaluate the employed models using four evaluation criteria: sum of square error, mean square error, R-square, and reliability. The experimental results showed that for the first version of the open-source dataset SRGM-4 based on imperfect debugging methodology achieved the best reliability result, and for the last two versions of the open-source dataset SRGM-6 based on Gompertz distribution methodology achieved the best reliability result in terms of sum of square error, mean square error, and R-square.

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“…• Perfect debugging methodology [25]: is the detection process of the faults without incorporating additional faults. This methodology contains only one case.…”

mentioning

confidence: 99%

“…Case 1 [25]: the function of fault content represented by α(N), which equals constant a. This case means debugging process will not cause new faults.…”

mentioning

confidence: 99%

“…• Imperfect debugging methodology [25]: is the possibility of introducing new faults while fixing the previous one. This methodology contains four cases.…”

mentioning

confidence: 99%

“…Case 2 [25]: the number of faults is a linear function of the user's number as the following equation:…”

mentioning

confidence: 99%

“…Case 3 [25]: the faults' number introduced exponentially related to the number of users according to the following equation:…”

mentioning

confidence: 99%

See 3 more Smart Citations

Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Ramadan¹,

Harb²,

Mousa³

et al. 2022

IJITCS

View full text Add to dashboard Cite

show abstract

Stochastic Differential Equation-Based Testing Coverage SRGM by Using ANN Approach

Bibyan,

Anand,

Aggarwal

et al. 2023

Lecture Notes in Networks and Systems

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Software Reliability Growth Model with Rate of Change in Application Characteristics

Mehta

Sharma

Tandon

2022

RACSC

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Background:: There has been continuous advancement in technologies for past few decades by incorporating new features in accordance to the market demand. The evolution of software projects/applications has intricated debugging process by generating numerous faults in it. Objective:: In this study, an attempt is made to develop a software reliability growth model (SRGM) taking into account the software project/application’s characteristic such as complexity of code and testing environment. The simulation is based on previous fault data in order to foresee the future latent faults occurring in the system for a given time frame. This model not only forecast the number of faults but is an extended version of Kapur and Garg’s error removal phenomenon model incorporating factors that might have influence on the model. Methods:: The performance of the model is validated using three data sets and finally compared with extant models, namely GO model and Yamada model to assess the proposed model’s efficiency. Results:: The parameter estimations were significant and the proposed model performed better in comparison to the other two models. Conclusion:: The proposed model is a contribution to the studies on the reliability of the project and can be extended in future by generalizing the results over various datasets and models.

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Reliability Modeling of OSS Systems based on Innovation-Diffusion Theory and Imperfect Debugging

Abstract: Abstract-Open

Cited by 3 publications

References 17 publications

Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Stochastic Differential Equation-Based Testing Coverage SRGM by Using ANN Approach

Software Reliability Growth Model with Rate of Change in Application Characteristics

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