The thirty greatest reliability challenges

Plessis, Annelize du; Frank, Karen M.; Saglimbene, Mark; Ozarin, Nathaniel

doi:10.1109/rams.2014.6798478

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…In Figure 5, we further experimented with different regularization rates to adjust the goodness of fit of the RNNs, which is based on the parameter λ in (3). We got the evaluation result 95.466% for jEdit 4.3 with η � 0.05, λ � 0.02, which is very close to the calculation result of Huang's model (96.224%).…”

Section: Resultsmentioning

confidence: 60%

“…(i) For a certain version, if a class has bug commit, it is defined as DPC (1) v4.0 x (2) v4.1 x (3) v4.2 x (4) v4.3 (ii) If a metric data of a class is obviously abnormal, the class is defined as DPC under the category including the metric as listed in Table 3 We assume that the historical version is aware of bug reports when we set jEdit 4.3 and Ant 1.7 as the current versions to be evaluated. is means that the classes in the current version are under development and there are no bug reports yet.…”

Section: Aggregation Strategiesmentioning

confidence: 99%

“…Software reliability engineering aims to improve software quality and its role covers software life cycle. Recently, more research studies believe that reliability engineering implemented in the early stages of software development can significantly reduce potential risks such as rework costs from later stages [1][2][3][4]. Structural reliability models work in the early reliability engineering, but they are often difficult to be applied in actual projects due to lack of modeling parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applying Software Metrics to RNN for Early Reliability Evaluation

Zhang

Shi

et al. 2020

Journal of Control Science and Engineering

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Structural modeling is an important branch of software reliability modeling. It works in the early reliability engineering to optimize the architecture design and guide the later testing. Compared with traditional models using test data, structural models are often difficult to be applied due to lack of actual data. A software metrics-based method is presented here for empirical studies. The recurrent neural network (RNN) is used to process the metric data to identify defeat-prone code blocks, and a specified aggregation scheme is used to calculate the module reliability. Based on this, a framework is proposed to evaluate overall reliability for actual projects, in which algebraic tools are introduced to build the structural reliability model automatically and accurately. Studies in two open-source projects show that early evaluation results based on this framework are effective and the related methods have good applicability.

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Section: Resultsmentioning

confidence: 60%

Section: Aggregation Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying Software Metrics to RNN for Early Reliability Evaluation

Zhang

Shi

et al. 2020

Journal of Control Science and Engineering

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Assessing changes in reliability methods over time: An unsupervised text mining approach

Brown,

Cameron

2024

Quality & Reliability Eng

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Reliability engineering faces many of the same challenges today that it did at its inception in the 1950s. The fundamental issue remains uncertainty in system representation, specifically related to performance model structure and parameterization. Details of a design are unavailable early in the development process and therefore performance models must either account for the range of possibilities or be wrong. Increasing system complexity has compounded this uncertainty. In this work, we seek to understand how the reliability engineering literature has shifted over time. We exe cute a systematic literature review of 30,543 reliability engineering papers (covering roughly a third of the reliability papers indexed by Elsevier's Engineering Village. Topic modeling was performed on the abstracts of those papers to identify 279 topics. The hierarchical topic reduction resulted in the identification of eight top‐level method topics (prognostics, statistics, maintenance, quality control, management, physics of failure, modeling, and risk assessment) as well as three domain‐specific topics (nuclear, infrastructure, and software). We found that topics more associated with later phases in the development process (such as prognostics, maintenance, and quality control) have increased in popularity over time relative to other topics. We propose that this is a response to the challenges posed by model uncertainty and increasing complexity.

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Empirical Research on the Application of a Structure-Based Software Reliability Model

Zhang

Shi

2021

IEEE/CAA J. Autom. Sinica

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The thirty greatest reliability challenges

Cited by 3 publications

References 5 publications

Applying Software Metrics to RNN for Early Reliability Evaluation

Applying Software Metrics to RNN for Early Reliability Evaluation

Assessing changes in reliability methods over time: An unsupervised text mining approach

Empirical Research on the Application of a Structure-Based Software Reliability Model

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