A URI parsing technique and algorithm for anti-pattern detection in RESTful Web services

Author:

Alshraiedeh Fuad Sameh,Katuk Norliza

Abstract

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.

Publisher

Emerald

Subject

Computer Networks and Communications,Information Systems

Reference54 articles.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Approach to Generating API Test Scripts Using GPT;Proceedings of the 12th International Symposium on Information and Communication Technology;2023-12-07

2. Machine learning with word embedding for detecting web-services anti-patterns;Journal of Computer Languages;2023-06

3. Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs;2022 IEEE International Conference on Services Computing (SCC);2022-07

4. Combinatorial testing of RESTful APIs;Proceedings of the 44th International Conference on Software Engineering;2022-05-21

5. Improving detection of web service antipatterns using crowdsourcing;The Journal of Supercomputing;2021-10-18

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