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Journal articles on the topic 'Antipattern'

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Author: Grafiati

Published: 10 March 2023

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1

Settas, Dimitrios L., Sulayman K. Sowe, and Ioannis G. Stamelos. "Detecting similarities in antipattern ontologies using semantic social networks: implications for software project management." Knowledge Engineering Review 24, no. 3 (September 2009): 287–307. http://dx.doi.org/10.1017/s0269888909990075.

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AbstractOntology has been recently proposed as an appropriate formalism to model software project management antipatterns, in order to encode antipatterns in a computer understandable form and introduce antipatterns to the Semantic Web. However, given two antipattern ontologies, the same entity can be described using different terminology. Therefore, the detection of similar antipattern ontologies is a difficult task. In this paper, we introduce a three-layered antipattern semantic social network, which involves the social network, the antipattern ontology network and the concept network. Social Network Analysis (SNA) techniques can be used to assist software project managers in finding similar antipattern ontologies. For this purpose, SNA measures are extracted from one layer of the semantic social network to another and this knowledge is used to infer new links between antipattern ontologies. The level of uncertainty associated with each new link is represented using Bayesian Networks (BNs). Furthermore, BNs address the issue of quantifying the uncertainty of the data collected regarding antipattern ontologies for the purposes of the conducted analysis. Finally, BNs are used to augment SNA by taking into account meta-information in their calculations. Hence, other knowledge not included in the social network can be used in order to search the social network for further inference. The benefits of using an antipattern semantic social network are illustrated using an example community of software project management antipattern ontologies.

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2

Zmeev, D. O., O. A. Zmeev, and L. S. Ivanova. "Antipattern Practice for Essence Practice Library." Programmnaya Ingeneria 13, no. 7 (August 22, 2022): 311–21. http://dx.doi.org/10.17587/prin.13.311-321.

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The Essence graphical representation language allows to describe various project management practices in software development. At the moment, the Practice Library describes the most popular development methodologies, but work with various risks, such as code smells or antipatterns, which may be cause of future problems, is not represented. This article presents an extension for the Practice Library of the Essence language in the form of a practice for working with antipatterns. To represent antipatterns in the system, the Antipattern subalpha, its states and checkpoints are proposed. Antipatterns states and checkpoints: 1. Detected: Рѕ Signs of poor-quality design or implementation of the Software System have been found. Рѕ The boundaries of the low-quality area in the Software System have been defined. Рѕ Work Products associated with the inferior part of the Software System have been defined. 2. Identified: Рѕ The Software System Elements included in the Antipattern have been determined. Рѕ The type of antipattern has been determined. Рѕ Dependencies of the Software System on the antipattern elements have been determined. Рѕ The impact of the antipattern on the Software System has been estimated. 3. Refactoring Method Defined: Рѕ Possible options for refactoring have been identified. Рѕ The cost of the refactoring options has been assessed. Рѕ The most suitable refactoring method has been selected. 4. Fixed: Рѕ The Software System elements included in the antipattern are completely covered by tests. Рѕ The antipattern has been refactored. Рѕ Tests confirm that the Software System remains operational. Рѕ The Work Products related to the changed part of the Software System have been updated. 5. Closed: Рѕ Tests, reviews or other appropriate activities have been undertaken to ensure that the antipattern has been corrected or shown not to actually be a fault or flaw. Рѕ The antipattern management has been finalized. Рѕ Technical debt has decreased. To record data about an antipattern, the work product Antipattern Report and its levels of details with checkpoints are proposed. Levels of details and checkpoints are: 1. Antipattern detected: Рѕ The components of the Software System included in the antipattern are defined. 2. Antipatterns properties are determined: Рѕ An antipattern type has been defined. 3. Antipattern influence determined: Рѕ The cost of antipattern fixing was estimated. Рѕ An assessment of possible losses associated with the presence of an antipattern was carried out. Рѕ The antipattern is recognized as mandatory for elimination. 4. Antipattern eliminated вЂ” optional: Рѕ Refactoring completed. Рѕ Testing showed the correct work of the components included in the antipattern. Рѕ Antipattern elimination activity completed. To analyze the architecture of the system, the Inspect Architecture activity is proposed. The Fix Architecture activity represents actions to fix architecture flaws. Code analysis is presented as a Review the Code activity, and correction of deficiencies found during the analysis is presented as a Refactor the Code activity. The influence of subalpha on the state of the Software System alpha is analyzed. Recommendations were given on the proposed activities. Information about activities is presented in the form of diagrams in the Essence language. The proposed practice allows to record information about the found flaws in the code, process them correctly and avoid problems with the project in the future. It is an example of working with entities that negatively affect the progress of the project.

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3

Saluja, Shivani, and Usha Batra. "Classification and Analysis of Static Metric Based Antipattern Detection in Service Computing." Recent Advances in Computer Science and Communications 13, no. 4 (October 19, 2020): 747–57. http://dx.doi.org/10.2174/2213275912666190809105751.

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Background: Design Pattern is regarded as an essential component for enhancement of system design which can further improve the reusability and maintainability whereas antipattern degrades the quality of the program. Antipatterns are sub-optimal implementation choices which initially appears to be a good concept but later on leads to low code maintainability and higher maintenance costs. Objective: The identification of antipatterns which lead to performance degradation plays an important role in the reduction of expensive work and cost involved in maintenance, redesign, reimplementation, and redeployment. The main motivation is to refactor the source code in order to reduce maintenance efforts. Antipatterns impact reliability, testability and maintainability, but they still lack clear identification because of different interpretations and definition of each antipattern. There is a need for a benchmark to analyze the result generated by antipatterns. Methods: Static and dynamic analysis individually do not suffice for antipattern .A hybrid approach is proposed by combining rule based static analysis with dynamic run time analysis. Before applying the hybrid approach a simple manual validation was done to exclude the type of input which had the least probability of containing antipattern. The approach aims at optimizing the results by inclusion of response time metric measure which can be evaluated at runtime execution of the web service. Results and Conclusion: : The paper focusses on detection of antipatterns from web services based on code level and interface level static metrics .Only a brief overview of dynamic approach for detection is proposed. The future scope of this paper will focus on detection of antipattern based on more number of dynamic metrics and also a comparative analysis of the results generated from static, dynamic and hybrid approach.

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4

Cortellessa, Vittorio, Daniele Di Pompeo, Vincenzo Stoico, and Michele Tucci. "Software Model Refactoring Driven by Performance Antipattern Detection." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 53–58. http://dx.doi.org/10.1145/3543146.3543159.

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The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, determining the primary causes of performance degradation is generally challenging, as they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis seems to show limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. In this tool paper we introduce PADRE (Performance Antipattern Detection and REfactoring), a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, which is an open-source platform for model-driven engineering, and it grounds on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

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5

Cortellessa, Vittorio, Daniele Di Pompeo, Vincenzo Stoico, and Michele Tucci. "Software Model Refactoring Driven by Performance Antipattern Detection." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 53–58. http://dx.doi.org/10.1145/3543146.3543159.

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The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, determining the primary causes of performance degradation is generally challenging, as they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis seems to show limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. In this tool paper we introduce PADRE (Performance Antipattern Detection and REfactoring), a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, which is an open-source platform for model-driven engineering, and it grounds on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

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6

Bán, Dénes. "The Connection between Antipatterns and Maintainability in Firefox." Acta Cybernetica 23, no. 2 (2017): 471–90. http://dx.doi.org/10.14232/actacyb.23.2.2017.3.

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The notion that antipatterns have a detrimental effect on source code maintainability is widely accepted, but there is relatively little objective evidence to support it. We seek to investigate this issue by analyzing the connection between antipatterns and maintainability in an empirical study of Firefox, an open source browser application developed in C++. After extracting antipattern instances and maintainability information from 45 revisions, we looked for correlations to uncover a connection between the two concepts. We found statistically significant negative values for both Pearson and Spearman correlations, most of which were under -0.65. These values suggest there are strong, inverse relationships, thereby supporting our initial assumption that the more antipatterns the source code contains, the harder it is to maintain. Lastly, we combined these data into a table applicable for machine learning experiments, which we conducted using Weka [10] and several of its classifier algorithms. All five regression types we tried had correlation coefficients over 0.77 and used mostly negative weights for the antipattern predictors in the models we constructed. In conclusion, we can say that this empirical study is another step towards objectively demonstrating that antipatterns have an adverse effect on software maintainability.

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7

Dhanaraj, Rajesh Kumar, Vinothsaravanan Ramakrishnan, M. Poongodi, Lalitha Krishnasamy, Mounir Hamdi, Ketan Kotecha, and V. Vijayakumar. "Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data." Wireless Communications and Mobile Computing 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/2730246.

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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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8

Conklin, Darrell. "Antipattern Discovery in Folk Tunes." Journal of New Music Research 42, no. 2 (June 2013): 161–69. http://dx.doi.org/10.1080/09298215.2013.809125.

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9

NAVARRO, ELENA, CARLOS E. CUESTA, DEWAYNE E. PERRY, and PASCUAL GONZÁLEZ. "ANTIPATTERNS FOR ARCHITECTURAL KNOWLEDGE MANAGEMENT." International Journal of Information Technology & Decision Making 12, no. 03 (May 2013): 547–89. http://dx.doi.org/10.1142/s0219622013500211.

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Recent research on Software Architecture has recovered its original emphasis on keeping track of design decisions and their rationales during software development, compiling them under the name of architectural knowledge (AK). This knowledge is composed of decision assets, which relate to each other creating a decision network structure. We argue that relationships in these networks of AK contain valuable information, in particular when they describe negative semantics. We use antipatterns to exploit and manage these negative relationships systematically. After examining and classifying the kinds of AK relationships, we describe a method that enriches this network by means of antipattern structures. To show the feasibility and suitability of this approach, we provide a proof-of-concept by applying it to an existing process, ATRIUM, with a concrete example, the Gas Station metaphor. Results of the use of the presented approach into three different projects with different complexities show both its feasibility and applicability.

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10

Müller-Frączek, Iwona. "Proposition of Synthetic Measure." Przegląd Statystyczny 64, no. 4 (December 31, 2017): 421–36. http://dx.doi.org/10.5604/01.3001.0014.0830.

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The aim of the study was to propose a new method for construction of synthetic variable. We analyzed distance between object and pattern (antipattern) in relation to sum of all distances between the pattern (antipattern) and other objects. This allowed us to characterize position of a region among others. The described measure is limited. Diagnostic variables do not require normalization or conversion to stimulants. Its main advantage is comparability of analyzes carried out for various years. The proposed method was illustrated by an empirical example. The values of measurement were compared with those obtained by Hellwig’s method. They were similar but not identical. Then two methods were compared in terms of correctness. None of them had a significant advantage.

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11

Settas, Dimitrios, Antonio Cerone, and Stefan Fenz. "Enhancing ontology-based antipattern detection using Bayesian networks." Expert Systems with Applications 39, no. 10 (August 2012): 9041–53. http://dx.doi.org/10.1016/j.eswa.2012.02.049.

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12

Saluja, Shivani, and Usha Batra. "Optimized approach for antipattern detection in service computing architecture." Journal of Information and Optimization Sciences 40, no. 5 (July 4, 2019): 1069–80. http://dx.doi.org/10.1080/02522667.2019.1638000.

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13

Trubiani, Catia, Alexander Bran, André van Hoorn, Alberto Avritzer, and Holger Knoche. "Exploiting load testing and profiling for Performance Antipattern Detection." Information and Software Technology 95 (March 2018): 329–45. http://dx.doi.org/10.1016/j.infsof.2017.11.016.

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14

Settas, Dimitrios L., Georgios Meditskos, Ioannis G. Stamelos, and Nick Bassiliades. "SPARSE: A symptom-based antipattern retrieval knowledge-based system using Semantic Web technologies." Expert Systems with Applications 38, no. 6 (June 2011): 7633–46. http://dx.doi.org/10.1016/j.eswa.2010.12.097.

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15

Harskyi, Oleksandr, and Svitlana Didyk. "Anti-patterns in criminal proceedings: forms of fixation in criminal proceedings." Slovo of the National School of Judges of Ukraine, no. 3(36) (November 17, 2021): 100–116. http://dx.doi.org/10.37566/2707-6849-2021-3(36)-8.

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The form of the evidentiary process also determines the form of administration of justice. The question of which was investigated in the article is a usual aspiration to improve the process of proving, thanks to the change of the procedural form of fixation in criminal proceedings. So far, the main means of fixing the course and results of a procedural action is a protocol. Even if the legislator necessarily provides video recording as an alternative form of fixation (for example, during the search), the protocol remains the main means of documenting the procedural actions of the body of pre-trial investigation. In the article the author tries to show the dependence of criminal procedural activity of subjects of proving and the court from the protocol, the influence of this means to the modern process of proving and makes a conclusion that the mentioned «pattern» is a bad decision in law-enforcement activity, that is antipattern. In addition, the evolution of the protocol as a form of fixation in criminal cases (pre-revolutionary period and the Soviet time), as well as a certain comparison of the existing procedural legislation of Ukraine and Western European countries in terms of forms of fixation in criminal proceedings and the impact of such forms on the process of proving is studied in a certain way. With the development of technology, as well as with the existing modern digital technology, including in the daily life of every person, it would be obvious to change this form of recording of criminal proceedings to audio-video recording. In addition to the effectiveness of such a means, the saving of procedural time of the preliminary investigation and the court should be taken into account. As for the issue of informativeness and preservation of information for the future, the advantages in such a form are obvious. Behind the antipattern there is always a place for a pattern, which, due to reasonable patterns, will be able to provide the state policy in the field of criminal prosecution with effective forms of means of proof. Keywords: criminal procedure, criminal procedure, criminal procedural activity, forms of proof, anti-pattern.

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PALMA, FRANCIS, MATHIEU NAYROLLES, NAOUEL MOHA, YANN-GAËL GUÉHÉNEUC, BENOIT BAUDRY, and JEAN-MARC JÉZÉQUEL. "SOA ANTIPATTERNS: AN APPROACH FOR THEIR SPECIFICATION AND DETECTION." International Journal of Cooperative Information Systems 22, no. 04 (December 2013): 1341004. http://dx.doi.org/10.1142/s0218843013410049.

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Like any other large and complex software systems, Service-Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions in their architecture, called antipatterns, in opposition to design patterns, which are good solutions to recurring problems. Antipatterns resulting from these changes may hinder the future maintenance and evolution of SBSs. The detection of antipatterns is thus crucial to assess the design and QoS of SBSs and facilitate their maintenance and evolution. However, methods and techniques for the detection of antipatterns in SBSs are still in their infancy despite their importance. In this paper, we introduce a novel and innovative approach supported by a framework for specifying and detecting antipatterns in SBSs. Using our approach, we specify 10 well-known and common antipatterns, including Multi Service and Tiny Service, and automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall two systems developed independently, (1) Home-Automation, an SBS with 13 services, and (2) FraSCAti, an open-source implementation of the Service Component Architecture (SCA) standard with more than 100 services. This validation demonstrates that our approach enables the specification and detection of Service Oriented Architecture (SOA) antipatterns with an average precision of 90% and recall of 97.5%.

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17

Arshanty, Reza Aprillia, Umi Sa'adah, Desy Intan Permata Sari, and Maulidan Bagus Afridian Rasyid. "Pengembangan Aplikasi People Management dalam Software Development dengan Pendekatan Project Management Antipatterns." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 4 (December 10, 2020): 343–53. http://dx.doi.org/10.22146/jnteti.v9i4.461.

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Faktor manusia dalam proses pengembangan perangkat lunak menjadi salah satu penentu keberhasilan suatu proyek, terlepas dari teknologi dan tools yang digunakan. Dalam praktiknya, banyak project manager tidak memiliki dataset mengenai hard skill dan soft skill anggotanya. Hal ini mengantarkan tim proyek pengembangan perangkat lunak terjebak dalam situasi antipatterns irrational management. Berdasarkan beberapa faktor utama penyebab kegagalan proyek pengembangan perangkat lunak, diperlukan adanya suatu mitigation plan yang dapat membantu project manager dalam melakukan manajemen sumber daya manusia dalam sebuah tim. Mitigation plan yang dapat dilakukan adalah dengan mengembangkan aplikasi people management yang didasarkan pada prinsip-prinsip Project Management Antipatterns. Makalah ini mengajukan suatu pemodelan baru dalam bentuk aplikasi untuk melakukan people management di dalam pengembangan perangkat lunak dengan pendekatan Project Management Antipatterns. Penelitian ini mengajukan suatu pemodelan baru dalam bentuk aplikasi untuk melakukan people management di dalam pengembangan perangkat lunak dengan pendekatan Project Management Antipatterns. Hasil yang diberikan berupa pemetaan tim developer kepada task yang relevan berdasarkan hard skill dan soft skill yang dimiliki. Selain itu, aplikasi ini juga memberikan hasil berupa rekomendasi treatment yang bisa dilakukan terhadap tim untuk meningkatkan produktivitas serta mengurangi potensi risiko yang ditimbulkan dari hasil diagnosis praktik yang buruk. Pendekatan yang digunakan terbukti mampu meningkatkan produktivitas tim secara keseluruhan. Refactored solution yang direkomendasikan juga terbukti efektif menurunkan nilai antipatterns dari satu sprint ke sprint berikutnya.

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18

Matthews, Jeanna. "Patterns and Anti-Patterns, Principles and Pitfalls: Accountability and Transparency in AI." AI Magazine 41, no. 1 (April 13, 2020): 82–89. http://dx.doi.org/10.1609/aimag.v41i1.5204.

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This article discusses a set of principles for accountability and transparency in artificial intelligence as well as a set of antipatterns or harmful trends too often seen in deployed systems. It provides concrete suggestions for what can be done to shift the balance away from these antipatterns and toward more positive ones.

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19

Palma, Francis, Javier Gonzalez-Huerta, Mohamed Founi, Naouel Moha, Guy Tremblay, and Yann-Gaël Guéhéneuc. "Semantic Analysis of RESTful APIs for the Detection of Linguistic Patterns and Antipatterns." International Journal of Cooperative Information Systems 26, no. 02 (May 16, 2017): 1742001. http://dx.doi.org/10.1142/s0218843017420011.

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Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

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20

Long, John. "Software reuse antipatterns." ACM SIGSOFT Software Engineering Notes 26, no. 4 (July 2001): 68–76. http://dx.doi.org/10.1145/505482.505492.

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21

Baker, David, and Tom Costello. "Your Hollywood Guide to Antipatterns." IT Professional 14, no. 3 (May 2012): 64–63. http://dx.doi.org/10.1109/mitp.2012.63.

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22

Feineman, David R. "Antipatterns: A Tool for Continuous Improvement." SPE Economics & Management 2, no. 02 (October 1, 2010): 64–69. http://dx.doi.org/10.2118/128596-pa.

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23

Klotins, Eriks, Michael Unterkalmsteiner, and Tony Gorschek. "Software Engineering Antipatterns in Start-Ups." IEEE Software 36, no. 2 (March 2019): 118–26. http://dx.doi.org/10.1109/ms.2018.227105530.

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24

Ouni, Ali, Marouane Kessentini, Katsuro Inoue, and Mel O. Cinneide. "Search-Based Web Service Antipatterns Detection." IEEE Transactions on Services Computing 10, no. 4 (July 1, 2017): 603–17. http://dx.doi.org/10.1109/tsc.2015.2502595.

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25

Arzamasova, Natalia, Martin Schaler, and Klemens Bohm. "Cleaning Antipatterns in an SQL Query Log." IEEE Transactions on Knowledge and Data Engineering 30, no. 3 (March 1, 2018): 421–34. http://dx.doi.org/10.1109/tkde.2017.2772252.

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26

Laplante, Phil, Robert Hoffman, and Gary Klein. "Antipatterns in the Creation of Intelligent Systems." IEEE Intelligent Systems 22, no. 1 (January 2007): 91–95. http://dx.doi.org/10.1109/mis.2007.3.

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27

Esmaeilyfard, Rasool. "Improving detection of web service antipatterns using crowdsourcing." Journal of Supercomputing 78, no. 5 (October 18, 2021): 6340–70. http://dx.doi.org/10.1007/s11227-021-04134-3.

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28

Boroday, S., A. Petrenko, J. Singh, and H. Hallal. "Dynamic analysis of java applications for multithreaded antipatterns." ACM SIGSOFT Software Engineering Notes 30, no. 4 (July 2005): 1–7. http://dx.doi.org/10.1145/1082983.1083247.

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Neill, Colin J. "9.1.2 Antipatterns in Systems Engineering: An Opening Trio." INCOSE International Symposium 22, no. 1 (July 2012): 1233–45. http://dx.doi.org/10.1002/j.2334-5837.2012.tb01399.x.

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Parsons, Trevor, and John Murphy. "Detecting Performance Antipatterns in Com-ponent Based Enterprise Systems." Journal of Object Technology 7, no. 3 (2008): 55. http://dx.doi.org/10.5381/jot.2008.7.3.a1.

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Palma, Francis, Naouel Moha, and Yann-Gael Gueheneuc. "UniDoSA: The Unified Specification and Detection of Service Antipatterns." IEEE Transactions on Software Engineering 45, no. 10 (October 1, 2019): 1024–53. http://dx.doi.org/10.1109/tse.2018.2819180.

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32

El-Attar, Mohamed, and James Miller. "Improving the quality of use case models using antipatterns." Software & Systems Modeling 9, no. 2 (February 15, 2009): 141–60. http://dx.doi.org/10.1007/s10270-009-0112-9.

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33

Laplante, Phillip. "The Burning Bag of Dung and Other Environmental Antipatterns." Queue 2, no. 7 (October 2004): 80–81. http://dx.doi.org/10.1145/1035594.1035617.

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Das, Tuhin Kanti, and Juergen Dingel. "Model development guidelines for UML-RT: conventions, patterns and antipatterns." Software & Systems Modeling 17, no. 3 (July 23, 2016): 717–52. http://dx.doi.org/10.1007/s10270-016-0549-6.

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35

Bola�os Castro, Sandro Javier, Rub�n Gonz�lez Crespo, and Victor Hugo Medina Garc�a. "Antipatterns: A Compendium of Bad Practices in Software Development Processes." International Journal of Interactive Multimedia and Artificial Intelligence 1, no. 4 (2011): 41. http://dx.doi.org/10.9781/ijimai.2011.147.

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36

Arnaoudova, Venera, Massimiliano Di Penta, and Giuliano Antoniol. "Linguistic antipatterns: what they are and how developers perceive them." Empirical Software Engineering 21, no. 1 (January 29, 2015): 104–58. http://dx.doi.org/10.1007/s10664-014-9350-8.

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37

Malik, Ivan Yuriyovich, Valeriy Yuriyovich Volovshchykov, Vladlen Felixovitch Shapo, and Marina Anatoliyvna Grinchenko. "TECHNOLOGY OF IDENTIFYING ANTIPATTERNS IN ANDROID PROJECTS WRITTEN IN KOTLIN LANGUAGE." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (3) (July 9, 2020): 117–23. http://dx.doi.org/10.20998/2079-0023.2020.01.19.

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38

Jamilah Din, Anas Bassam ALBadareen, Yusmadi Yah Jusoh, and Azrina Kamaruddin. "A Review of the Antipatterns Detection Approaches in Object-Oriented Design." Journal of Convergence Information Technology 8, no. 6 (March 31, 2013): 518–27. http://dx.doi.org/10.4156/jcit.vol8.issue6.62.

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39

Arcelli, Davide, and Daniele Di Pompeo. "Applying Design Patterns to Remove Software Performance Antipatterns: A Preliminary Approach." Procedia Computer Science 109 (2017): 521–28. http://dx.doi.org/10.1016/j.procs.2017.05.330.

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40

Trubiani, Catia, Anne Koziolek, Vittorio Cortellessa, and Ralf Reussner. "Guilt-based handling of software performance antipatterns in palladio architectural models." Journal of Systems and Software 95 (September 2014): 141–65. http://dx.doi.org/10.1016/j.jss.2014.03.081.

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41

Khomh, Foutse, Stephane Vaucher, Yann-Gaël Guéhéneuc, and Houari Sahraoui. "BDTEX: A GQM-based Bayesian approach for the detection of antipatterns." Journal of Systems and Software 84, no. 4 (April 2011): 559–72. http://dx.doi.org/10.1016/j.jss.2010.11.921.

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42

De Sanctis, Martina, Catia Trubiani, Vittorio Cortellessa, Antinisca Di Marco, and Mirko Flamminj. "A model-driven approach to catch performance antipatterns in ADL specifications." Information and Software Technology 83 (March 2017): 35–54. http://dx.doi.org/10.1016/j.infsof.2016.11.008.

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43

Khan, Yasser A., and Mohamed El-Attar. "Using model transformation to refactor use case models based on antipatterns." Information Systems Frontiers 18, no. 1 (August 13, 2014): 171–204. http://dx.doi.org/10.1007/s10796-014-9528-z.

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44

Trubiani, Catia, and Anne Koziolek. "Detection and solution of software performance antipatterns in palladio architectural models (abstracts only)." ACM SIGMETRICS Performance Evaluation Review 39, no. 3 (December 21, 2011): 11. http://dx.doi.org/10.1145/2160803.2160807.

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45

Kacalak, Wojciech, Maciej Majewski, Keith Douglas Stuart, and Zbigniew Budniak. "Interactive Systems for Designing Machine Elements and Assemblies." Management and Production Engineering Review 6, no. 3 (September 1, 2015): 21–34. http://dx.doi.org/10.1515/mper-2015-0023.

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Abstract:

Abstract The article describes the development of fundamentals of machine elements and assemblies design processes automation using artificial intelligence, and descriptions of structural elements’ features in a natural language. In the proposed interactive automated design systems, computational artificial intelligence methods allow communication by speech and natural language, resulting in analyses of design engineer’s messages, analyses of constructions, encoding and assessments of constructions, CAD system controlling and visualizations. The system is equipped with several adaptive intelligent layers for human biometric identification, recognition of speech and handwriting, recognition of words, analyses and recognition of messages, enabling interpretation of messages, and assessments of human reactions. The article proposes a concept of intelligent processing for analysis of descriptions of machine elements’ structural features in a natural language. It also presents the developed methodology for similarity analysis between structural features of designed machine elements and corresponding antipatterns allowing normalization of parameters of the analysed structural solutions.

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46

Di Marco, Antinisca, and Catia Trubiani. "A model-driven approach to broaden the detection of software performance antipatterns at runtime." Electronic Proceedings in Theoretical Computer Science 147 (April 2, 2014): 77–92. http://dx.doi.org/10.4204/eptcs.147.6.

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Cortellessa, Vittorio, Antinisca Di Marco, and Catia Trubiani. "An approach for modeling and detecting software performance antipatterns based on first-order logics." Software & Systems Modeling 13, no. 1 (April 21, 2012): 391–432. http://dx.doi.org/10.1007/s10270-012-0246-z.

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Khomh, Foutse, Massimiliano Di Penta, Yann-Gaël Guéhéneuc, and Giuliano Antoniol. "An exploratory study of the impact of antipatterns on class change- and fault-proneness." Empirical Software Engineering 17, no. 3 (August 6, 2011): 243–75. http://dx.doi.org/10.1007/s10664-011-9171-y.

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Abid, Chaima, Marouane Kessentini, and Hanzhang Wang. "Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns." Information and Software Technology 126 (October 2020): 106313. http://dx.doi.org/10.1016/j.infsof.2020.106313.

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Sreeji, K. S., and C. Lakshmi. "Dynamic Understanding of Software System Using Structural and Semantic Based Detection of Design Patterns and Antipatterns." Journal of Computational and Theoretical Nanoscience 15, no. 9 (September 1, 2018): 3037–43. http://dx.doi.org/10.1166/jctn.2018.7587.

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