FRWSC: a framework for robust Web service composition

2018

IET softw.

Due to the increasing scale and complexity of service-oriented systems (SOSs) understanding fault and its recovery mechanism is a tedious task so there is a strong demand for a summary of the SOSs fault analysis knowledge that can assist further developers and researchers to plan appropriate action and guidelines for SOSs development. The objective of this systematic literature review (SLR) is to summarise the current state-of-the-art of fault analysis in SOSs. The authors have used the predefined SLR method employing a manual search of 11 highly reputed international journals and three conference proceedings. The categorisation and organisation were done according to the research questions regarding approaches, fault types, current issues and challenges and tools of fault analysis on SOSs. After applying selection criteria, this SLR includes 123 papers published between January 2008 and April 2017, 65 papers address SOSs-fault handling approaches, 35 papers report SOS-specific faults and 51 papers address SOS issues, challenges, and testing and 17 papers are of tooling. They have discussed the main approaches, techniques, concepts, contributions and research methods used for fault analysis of SOSs. Through the paper, a comprehensive review work has been presented that provides empirical evidence for establishing future research agendas.

Section: Results Of the Systematic Reviewmentioning

confidence: 99%

Section: Results Of the Systematic Reviewmentioning

confidence: 99%

“…SOAP error messages cannot provide the WS client the sufficient information about service failure [71]. Current systems and platforms often generate logs in real time and in huge volume.…”

Section: Discussionmentioning

confidence: 99%

Section: Summary Of Reviewed Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Fault analysis of service‐oriented systems: a systematic literature review

2018

IET softw.

“…Their framework supports the study of how error-handling affects and how much time is taken for task-completion with error-fixing. Kholy and Fatatry (2016) have specified that Simple Object Access protocol (SOAP) error messages cannot provide sufficient information about service failure to web service client. The framework is supposed to provide a clear message to deal with composite service failure due to incorrect user parameters, bad network connections, and provider-side errors.…”

Section: Related Workmentioning

confidence: 99%

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

Upadhyay

2019

DTA

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.

Fault Prediction in SOA-Based Systems Using Deep Learning Techniques

International Journal of Web Services Research

2020

Fault prediction in Service Oriented Architecture (SOA) based systems is one of the important tasks to minimize the computation cost and time of the software system development. Predicting the faults and discovering their locations in the early stage of the system development lifecycle makes maintenance processes easy and improves the resource utilization. In this paper, the authors proposed the fault prediction model for SOA-based systems by utilizing the deep learning techniques. Twenty-one source code metrics are applied to different web services projects. The web services datasets are constructed by injecting the faults into it, and metrics are extracted for both faulty and nonfaulty data for training and testing purpose. Moreover, different deep learning techniques are inspected for fault prediction of web services and performance of different methods are compared by using standard performance measures. From the experimental results, it is observed that deep learning techniques provide effective results and applicable to the real-world SOA-based systems.