A self-learning approach for validation of runtime adaptation in service-oriented systems

Mutanu, Leah; Kotonya, Gerald

doi:10.1007/s11761-017-0222-0

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Instead of using the greedy layer-wise pre-training strategy, the classification layer can be joined to the training process to achieve model accuracy [33]. In order to implement this model in the real world, online-learning could be considered in the future [34], [35]. For the dynamic tuning of parameters, fuzzy inference systems [36], [37] and extreme learning machines [38] may also be used to further improve the proposed system.…”

Section: Discussionmentioning

confidence: 99%

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Lin

Chiang

et al. 2018

Future Generation Computer Systems

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Section: Discussionmentioning

confidence: 99%

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Lin

Chiang

et al. 2018

Future Generation Computer Systems

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“…Therefore, in this work, the term 'fuzzy learning' means a learning algorithm that uses fuzzy logic. Various studies also used a neural network in SAS [87,68].…”

Section: B Rq2 What ML Techniques Are Researchers and Practitionersmentioning

confidence: 99%

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Saputri

Lee

2020

IEEE Access

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Self-adaptive systems have been studied in software engineering over the past few decades attempting to address challenges within the field. There is a continuous significant need to fully understand the behavior and characteristics of the systems that operate in dynamic environments. By learning the behavior pattern of the environment, we can avoid unnecessary adaptations imbalance efforts for adaptation. As such, there exist research in the area of machine learning aimed at understanding dynamic environments regarding self-adaptive systems. Objective: This study aims to help software practitioners to address adaptation concerns by performing a systematic literature review that provides a comprehensive overview of using machine learning (ML) in self-adaptive systems. We summarize state-ofthe-art Of the ML approaches used to handle self-adaptation to help software engineers in the proper selection of ML techniques based on the adaptation concern. Method: This review examines research published between 2001 and 2019 on ML implementation in self-adaptive systems, focusing on the adaptation aspects and purposes. The review was conducted by analyzing major scientific databases that resulted in 78 primary studies from 315 papers from an automatic search. Result: Finally, this study recommends three future research directions to enhance the application of machine learning in self-adaptive systems.

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“…Similarly, He et al (2016) have presented a comparative study based on automated log-based anomaly detection methods using ML demonstrating the various log-based anomaly detection methods and experimentally evaluated them on two publicly available production log data sets. Mutanu and Kotonya (2018) have proposed a self-learning runtime adaptation approach through consumer-centric validation. The feasibility of this work is validated through model-based clustering and deep learning techniques where clustering is suitable for online validation and deep learning for off-line validation (Mutanu and Kotonya, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Mutanu and Kotonya (2018) have proposed a self-learning runtime adaptation approach through consumer-centric validation. The feasibility of this work is validated through model-based clustering and deep learning techniques where clustering is suitable for online validation and deep learning for off-line validation (Mutanu and Kotonya, 2018). Ismail et al (2011, 2013) have mentioned an approach for service level agreement (SLA) violation handling using incremental time impact analysis.…”

Section: Related Workmentioning

confidence: 99%

An approach for fault prediction in SOA-based systems using machine learning techniques

Bhandari

Gupta

Upadhyay

2019

DTA

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Purpose Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue. Design/methodology/approach In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures. Findings The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively. Research limitations/implications One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques. Originality/value In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

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A self-learning approach for validation of runtime adaptation in service-oriented systems

Cited by 5 publications

References 32 publications

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

An approach for fault prediction in SOA-based systems using machine learning techniques

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