NHPP Software Reliability Model with Inflection Factor of the Fault Detection Rate Considering the Uncertainty of Software Operating Environments and Predictive Analysis

Song, Kiwon; Chang, In Hong; Pham, Hoang

doi:10.3390/sym11040521

Cited by 20 publications

(9 citation statements)

References 29 publications

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“…The NHPP models assume defects that are discovered during the testing phase are removed without introducing new defects, and the mobile application used in the field environment is the same as that used during the testing phase. In this case study, we use the following three commonly used SRGMs and Song's newly proposed model et al [18]:…”

Section: Modeling Srgmsmentioning

confidence: 99%

“…where r is the measurement of the CPU-time execution, 0 λ is the intensity of the initial failure, and φ is a model parameter.  A newly proposed model, which was presented by Song et al [18] to measure software reliability while considering the uncertainty of operating systems and learn-curve in the fault detection rate function, is as follows:…”

Section: Modeling Srgmsmentioning

confidence: 99%

See 1 more Smart Citation

Assessment and Prediction of Software Reliability in Mobile Applications

Barack¹,

Huang²

2020

JSEA

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Software reliability is an important quality attribute, and software reliability models are frequently used to measure and predict software maturity. The nature of mobile environments differs from that of PC and server environments due to many factors, such as the network, energy, battery, and compatibility. Evaluating and predicting mobile application reliability are real challenges because of the diversity of the mobile environments in which the applications are used, and the lack of publicly available defect data. In addition, bug reports are optionally submitted by end-users. In this paper, we propose assessing and predicting the reliability of a mobile application using known software reliability growth models (SRGMs). Four software reliability models are used to evaluate the reliability of an open-source mobile application through analyzing bug reports. Our experiment proves it is possible to use SRGMs with defect data acquired from bug reports to assess and predict the software reliability in mobile applications. The results of our work enable software developers and testers to assess and predict the reliability of mobile software applications.

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Section: Modeling Srgmsmentioning

confidence: 99%

Section: Modeling Srgmsmentioning

confidence: 99%

Assessment and Prediction of Software Reliability in Mobile Applications

Barack¹,

Huang²

2020

JSEA

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“…Malavoltaet al [10] analyzed about different android application maintainability process and detecting life time of it. They evaluated android apps with more than 9,945 weekly snapshots for understanding and categorizing the level of maintainability.…”

Section: Related Workmentioning

confidence: 99%

Enhanced convolutional neural network based android software maintainability prediction

Balamurugan

Ravichandran

2022

ijhs

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Advancement in mobile technology, necessitates quality in development of android applications. To improve the quality of android application, evaluation of its maintenance has to be started from the initial stage of development. There are several existing models involved in android application maintenance evaluation, but most of them suffers from the class imbalance problem and its performance depends on the size of the dataset. This paper focuses on overcoming the class imbalance while using convolutional neural network for prediction the android application maintainability. In this work both android metrics and object-oriented metrics are used for evaluating the android application maintainability. The standard convolutional neural network is enhanced by pretraining it using evolutionary algorithm known as moth flame optimization (MFO). The MFO involves in optimizing the learning parameters of convolutional layer and after the training process is completed, with the acquired knowledge it handles the small size dataset very effectively during the testing phase. Thus, the simulation results of maintainability prediction in android application development using proposed enhanced CNN achieves better result while comparing with other standard classification models.

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“…Li et al in [25] proposed NHPP software reliability models considering fault removal efficiency and error generation, and the uncertainty of operating environments with imperfect debugging and testing coverage. Song et al in [34] studied NHPP software reliability models with various fault detection rates considering the uncertainty of operating environments. Zhu et al in [35] proposed a NHPP software reliability model with a pioneering idea by considering software fault dependency and imperfect fault removal.…”

Section: Introductionmentioning

confidence: 99%

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

Zonghao³

et al. 2020

IEEE Access

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Over the last several years, many testability growth models (TGMs) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of testability as system is being improved. Most TGMs consider only one or two variation patterns of the aspects, such as the testability growth effort (TGE) in testability design limitation (TDL) identification, the rectifying delay and the new TDL introduction in TDL correction. However, the ignorance of such joint consideration may lead to a lower fitting ability to the fault detection/isolation data. Inspired by the counting idea of nonhomogeneous Poisson process (NHPP), a NHPP based testability growth model (TGM) considering the recurrence rate function (RRF) of TDL identification, TDL correction and new TDL introduction is proposed for the foundation of TGM. A real data set of a missile control system is used to validate the above TGMs in fitting ability, estimation accuracy and prediction capability. Results show that the bell-shaped curve can fit the identification process and rectifying delay process of TDL well, and the imperfect correction of TDL really exists in the testability growth test (TGT), and the inflected s-shaped and Gamma function based TGM gives good capacity to the real data set. INDEX TERMS TGM, NHPP, testability growth effort, rectifying delay, imperfect correction.

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NHPP Software Reliability Model with Inflection Factor of the Fault Detection Rate Considering the Uncertainty of Software Operating Environments and Predictive Analysis

Cited by 20 publications

References 29 publications

Assessment and Prediction of Software Reliability in Mobile Applications

Assessment and Prediction of Software Reliability in Mobile Applications

Enhanced convolutional neural network based android software maintainability prediction

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

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