Web services provide a uniform framework to achieve a high level of interaction between distributed heterogeneous software systems and data resources shared over the Internet. Producing a well-designed web service is significant because it leads to a more understandable service and a higher level of interaction and leads to effective software maintainability. However, web service is suffering from a poor design problem named anti-patterns. Analysis of the literature returned a plethora of studies on anti-patterns that caused difficulties for developers to synthesize and summarized the possible types of anti-patterns and further comprehend each of them. Due to this limitation, this paper aims to provide organized literature on the types of anti-patterns found in web services. A scoping review was conducted by searching scholarly documents, analyzing, and classified them based on their anti-pattern types. The review provided in this paper could be used as a guide for developers to identify the antipatterns that could be found in web services.
Purpose Many REpresentational State Transfer (RESTful) Web services suffered from anti-patterns problem, which may diminish the sustainability of the services. The anti-patterns problem could happen in the code of the programme or the uniform resource identifiers (URIs) of RESTful Web services. This study aims to address the problem by proposing a technique and an algorithm for detecting anti-patterns in RESTful Web services. Specifically, the technique is designed based on URIs parsing process. Design/methodology/approach The study was conducted following the design science research process, which has six activities, namely, identifying problems, identifying solutions, design the solutions, demonstrate the solution, evaluation and communicate the solution. The proposed technique was embedded in an algorithm and evaluated in four phases covering the process of extracting the URIs, implementing the anti-pattern detection algorithm, detecting the anti-patterns and validating the results. Findings The results of the study suggested an acceptable level of accuracy for the anti-patterns detection with 82.30% of precision, 87.86% of recall and 84.93% of F-measure. Practical implications The technique and the algorithm can be used by developers of RESTful Web services to detect possible anti-pattern occurrences in the service-based systems. Originality/value The technique is personalised to detect amorphous URI and ambiguous name anti-patterns in which it scans the Web service URIs using specified rules and compares them with pre-determined syntax and corpus.
Abstract-Web Services are important for integrating distributed heterogeneous applications. One of the problems that facing Web Services is the difficulty for a service provider to represent the datatype of the parameters of the operations provided by a Web service inside Web Service Description Language (WSDL). This problem will make it difficult for service requester to understand, reverse engineering, and also to decide if Web service is applicable to the required task of their application or not. This paper introduces an approach to extend Web service datatypes specifications inside WSDL in order to solve the aforementioned challenges. This approach is based on adding more description to the provided operations parameters datatypes and also simplified the WSDL document in new enrichment XML-Schema. The main contributions of this paper are:1. Comprehensive study of 33 datatypes in C# language, and how they are represented inside WSDL document. Classification of the previous mentioned datatypes into
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.