Enhancing Software Reliability and Fault Detection Using Hybrid Brainstorm Optimization-Based LSTM Model

Raamesh, Lilly; Jothi, S.; Radhika, S.

doi:10.1080/03772063.2022.2069603

Cited by 12 publications

(2 citation statements)

References 36 publications

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“…Miyamoto [33] developed a software reliability model for software failures that occur in open-source software using deep neural networks (DNNs). In addition, because software failures have time-series characteristics, a software reliability model using long short-term memory (LSTM) was proposed [34,35]. Wu et al [36] proposed a structure in which the weights of the software reliability model are learned through the weights of the DNN.…”

Section: Related Researchmentioning

confidence: 99%

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Kim,

Pham,

Chang

2023

Applied Sciences

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Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.

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Section: Related Researchmentioning

confidence: 99%

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Kim,

Pham,

Chang

2023

Applied Sciences

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“…Among the nonparametric software reliability models using deep learning, a study on software reliability models using deep neural networks was conducted [17], and a software reliability model using deep learning that applies failure data generated through open-source software was also proposed [18]. In addition, since software failure is a sequential data characteristic, the software reliability model using the recurrent neural network (RNN) and the long short-term memory (LSTM) was studied using this characteristic [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning

2023

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Over time, software has become increasingly important in various fields. If the current software is more dependent than in the past and is broken owing to large and small issues, such as coding and system errors, it is expected to cause significant damage to the entire industry. To address this problem, the field of software reliability is crucial. In the past, efforts in software reliability were made to develop models by assuming a nonhomogeneous Poisson-process model (NHPP); however, as models became more complex, there were many special cases in which models fit well. Hence, this study proposes a software reliability model using deep learning that relies on data rather than mathematical and statistical assumptions. A software reliability model based on recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), which are the most basic deep and recurrent neural networks, was constructed. The dataset was divided into two, Datasets 1 and 2, which both used 80% and 90% of the entire data, respectively. Using 11 criteria, the estimated and learned results based on these datasets proved that the software reliability model using deep learning has excellent capabilities. The software reliability model using GRU showed the most satisfactory results.

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