A Lightweight Approach for Specification and Detection of  SOAP Anti-Patterns

Sabir, Fatima; Rasool, Ghulam; Yousaf, Maria

doi:10.14569/ijacsa.2017.080555

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…Impactando negativamente a qualidade dos artefatos e das demais atividades de desenvolvimento. No mais, os trabalhos de [9,44] somente citam a existência de Requirements Smells como um conceito investigado por [21].…”

Section: Resultados Da Buscaunclassified

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Nascimento¹,

Aranha

Kulesza

et al. 2018

Anais Do WER18 - Workshop Em Engenharia De Requisitos

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Section: Resultados Da Buscaunclassified

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Nascimento¹,

Aranha

Kulesza

et al. 2018

Anais Do WER18 - Workshop Em Engenharia De Requisitos

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“…Several methods were utilized for detecting antipatterns which involved a large query log database. In [12], metricbased techniques were designed. Yet, another flexible method was proposed in [13], and also a new online detection method was proposed in [14] for improving the performance of antipattern detection from query log database.…”

Section: Related Workmentioning

confidence: 99%

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Dhanaraj

Ramakrishnan

Poongodi

et al. 2021

Wireless Communications and Mobile Computing

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In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. However, more focus needs to be given to the accuracy aspect. In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. To address the above-said issues and further enhance the antipattern detection accuracy with minimum time and false positive rate, in this work, Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed. Different patterns or queries are initially gathered from the input SQL query log, and bootstrap samples are created. Then, for each pattern, various weak clusters are constructed via X-means clustering and are utilized as the weak learner (clusters). During this process, the input patterns are categorized into different clusters. Using the Bayesian information criterion, the similarity measure is employed to evaluate the similarity between the patterns and cluster weight. Based on the similarity value, patterns are assigned to either relevant or irrelevant groups. The weak learner results are aggregated to form strong clusters, and, with the aid of voting, a majority vote is considered for designing strong clusters with minimum time. Experiments are conducted to evaluate the performance of the RFBXSQLQC technique using the IIT Bombay dataset using the metrics like antipattern detection accuracy, time complexity, false-positive rate, and computational overhead with respect to the differing number of queries. The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.

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“…Palma et al (2015) and Palma et al (2017) proposed DOLAR and SARA, respectively, to detect linguistics anti-patterns in RESTful WS. Studies have been performed to detect the anti-patterns from software performance perspective such as Sabir et al (2017), Palma et al (2017), Kalyani (2018), Saluja and Batra (2019). Relevant studies on WS anti-patterns detection are listed in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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

Alshraiedeh

Katuk

2020

IJWIS

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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.

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A Lightweight Approach for Specification and Detection of SOAP Anti-Patterns

Cited by 7 publications

References 36 publications

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

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

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