Extended Fault Taxonomy of SOA-Based Systems

Bhandari, Guru Prasad; Gupta, Ratneshwer

doi:10.20532/cit.2017.1003569

Cited by 9 publications

(3 citation statements)

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“…Wang et al [7] have categorised fault types on the basis of four contexts such as functional context, QoS context, domain context and platform context faults. The fault taxonomy is presented in [7, 16, 48, 51, 97–99]. In these taxonomies, faults of SOS are categorised into two major categories such as SOA cycle‐specific faults and distributed system faults.…”

Section: Results Of the Systematic Reviewmentioning

confidence: 99%

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

Bhandari

Gupta

2018

IET softw.

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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.

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Section: Results Of the Systematic Reviewmentioning

confidence: 99%

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

Bhandari

Gupta

2018

IET softw.

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“…Fault recovery plays a crucial role in testing web service composition, enabling the creation of a more resilient and smoothly functioning system that can promptly recover from encountered faults [1,21] This, in turn, enhances availability, scalability, and composition quality, while minimizing costs and efforts under expected operational conditions [22,23]. Numerous studies have addressed fault tolerance and recovery strategies for Service-Oriented Computing (SOC) systems [1,13,14,21,22,24,25]. However, the majority of these studies focus on proposing recovery approaches for substituting the faulty service rather than considering any other recovery strategy.…”

Section: Related Workmentioning

confidence: 99%

A Recommender System for Fault Recovery Strategies in Web Services Composition Testing

ElGhondakly,

Moussa,

Badr

2023

IJICIS

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Faults recovery has recently emerged as an important aspect of web service composition (WSC) testing, as it aims to minimize the impact of faults on system functionality through restoring the system's operation after a fault has occurred. However, most of the existing recommendation systems (RSs) tend to recommend frequently used services, which lack diversity and face inaccuracies due to incomplete or biased historical data. In addition, the focus of the existing RSs is on proposing fault handling models rather than recommending the best recovery strategy for handling faults, with most being code-based, thus not suitable for WSCs. Accordingly, due to the opaque nature of WSCs with hidden source code, model-based recovery methods are preferred. In this paper, the Fault Recovery Strategies RS for WSCs (F2RS-WSC) is proposed to recommend the best recovery strategy for handling emerging faults in the WSCs paradigm. The proposed system is a model-based system that recommends the best strategy for recovering faulty paths generated from service dependency graphs (SDGs) based on the faults` types, severity levels, faults` location, as well as the time at which faults may occur. The experimental results show that the time consumed by F2RS-WSC to recommend the optimum recovery strategy represents less than 3% of the SDG parsing time and 4% of the path validation time. In addition, its superior performance assures its accuracy and efficiency. Thus, it achieves accuracy levels between 70% and 88 % among multiple datasets. Moreover, its average precision, recall and f-measure values are 0.85,0.81 and 0.86 respectively.

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“…One fault can escalate another serious fault or multiple faults and bring the systems to failure. Injecting a fault at the program level can be associated with the multiple faults at the application level, as specified in Bhandari and Gupta (2018a, b). For example, a fault in a method’s parameter can cause a messaging error, communication failures, invalid method invocation faults, etc.…”

Section: Research Backgroundmentioning

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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Extended Fault Taxonomy of SOA-Based Systems

Cited by 9 publications

References 59 publications

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

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

A Recommender System for Fault Recovery Strategies in Web Services Composition Testing

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

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