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Musaddiq, A., Maleki, N., Palma, F., Olsson, T., Toll, D., Mozart, D., . . . Ahlgren, F. (2023). Industry-Academia Cooperation: Applied IoT Research for SMEs in South-East Sweden. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L (Ed.), Internet of Things. GIoTS 2022: . Paper presented at 5th The Global IoT Summit, GIoTS 2022, Dublin, Ireland, June 20–23, 2022, Revised Selected Papers (pp. 397-410). Springer
Open this publication in new window or tab >>Industry-Academia Cooperation: Applied IoT Research for SMEs in South-East Sweden
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2023 (English)In: Internet of Things. GIoTS 2022 / [ed] González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L, Springer, 2023, p. 397-410Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Computer Science ; 13533
Keywords
IoT, SME, Pilot cases
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-118208 (URN)10.1007/978-3-031-20936-9_32 (DOI)2-s2.0-85147856168 (Scopus ID)9783031209352 (ISBN)9783031209369 (ISBN)
Conference
5th The Global IoT Summit, GIoTS 2022, Dublin, Ireland, June 20–23, 2022, Revised Selected Papers
Projects
IoT lab for SME
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-11-02Bibliographically approved
Sabir, F., Gueheneuc, Y.-G., Palma, F., Moha, N., Rasool, G. & Akhtar, H. (2022). A Mixed-Method Approach to Recommend Corrections and Correct REST Antipatterns. IEEE Transactions on Software Engineering, 48(11), 4319-4338
Open this publication in new window or tab >>A Mixed-Method Approach to Recommend Corrections and Correct REST Antipatterns
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2022 (English)In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 48, no 11, p. 4319-4338Article in journal (Refereed) Published
Abstract [en]

Many companies, e.g., Facebook and YouTube, use the REST architecture and provide REST APIs to their clients. Like any other software systems, REST APIs need maintenance and must evolve to improve and stay relevant. Antipatterns—poor design practices—hinder this maintenance and evolution. Although the literature defines many antipatterns and proposes approaches for their (automatic) detection, their correction did not receive much attention. Therefore, we apply a mixed-method approach to study REST APIs and REST antipatterns with the objectives to recommend corrections or, when possible, actually correct the REST antipatterns. Qualitatively, via case studies, we analyse the evolution of 11 REST APIs, including Facebook, Twitter, and YouTube, over six years. We detect occurrences of eight REST antipatterns in the years 2014, 2017, and 2020 in 17 versions of 11 REST APIs. Thus, we show that (1) REST APIs and antipatterns evolve over time and (2) developers seem to remove antipatterns. Qualitatively via a discourse analysis, we analyse developers’ forums and report that developers are concerned with the occurrences of REST antipatterns and discuss corrections to these antipatterns. Following these qualitative studies, using an engineering-research approach, we propose the following novel and unique contributions: (1) we describe and compare the corrections of eight REST antipatterns from the academic literature and from developers’ forums; (2) we devise and describe algorithms to recommend corrections to some of these antipatterns; (3) we present algorithms and a tool to correct some of these antipatterns by intercepting and modifying responses from REST APIs; and, (4) we validate the recommendations and the corrections manually and via a survey answered by 24 REST developers. Thus, we propose to REST API developers and researchers the first, grounded approach to correct REST antipatterns.

Place, publisher, year, edition, pages
IEEE, 2022
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-117578 (URN)10.1109/tse.2021.3117023 (DOI)000881981800005 ()2-s2.0-85116914527 (Scopus ID)
Available from: 2022-11-17 Created: 2022-11-17 Last updated: 2023-04-06Bibliographically approved
Ganesh, S., Palma, F. & Olsson, T. (2022). Are Source Code Metrics "Good Enough" in Predicting Security Vulnerabilities?. Data, 7(9), Article ID 127.
Open this publication in new window or tab >>Are Source Code Metrics "Good Enough" in Predicting Security Vulnerabilities?
2022 (English)In: Data, E-ISSN 2306-5729, Vol. 7, no 9, article id 127Article in journal (Refereed) Published
Abstract [en]

Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write code. In this study, we studied the ability of several machine learning algorithms to predict security vulnerabilities. We created two datasets containing security vulnerability information from two open-source systems: (1) Apache Tomcat (versions 4.x and five 2.5.x minor versions). We also computed source code metrics for these versions of both systems. We examined four classifiers, including Naive Bayes, Decision Tree, XGBoost Classifier, and Logistic Regression, to show their ability to predict security vulnerabilities. Moreover, an ensemble learner was introduced using a stacking classifier to see whether the prediction performance could be improved. We performed cross-version and cross-project predictions to assess the effectiveness of the best-performing model. Our results showed that the XGBoost classifier performed best compared to other learners, i.e., with an average accuracy of 97% in both datasets. The stacking classifier performed with an average accuracy of 92% in Struts and 71% in Tomcat. Our best-performing model-XGBoost-could predict with an average accuracy of 87% in Tomcat and 99% in Struts in a cross-version setup.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
prediction, security vulnerabilities, machine learning, source code, software metrics
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-116577 (URN)10.3390/data7090127 (DOI)000856302500001 ()2-s2.0-85138644516 (Scopus ID)
Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-08-28Bibliographically approved
Palma, F., Olsson, T., Wingkvist, A. & Gonzalez-Huerta, J. (2022). Assessing the linguistic quality of REST APIs for IoT applications. Journal of Systems and Software, 191, Article ID 111369.
Open this publication in new window or tab >>Assessing the linguistic quality of REST APIs for IoT applications
2022 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 191, article id 111369Article in journal (Refereed) Published
Abstract [en]

Internet of Things (IoT) is a growing technology that relies on connected 'things' that gather data from peer devices and send data to servers via APIs (Application Programming Interfaces). The design quality of those APIs has a direct impact on their understandability and reusability. This study focuses on the linguistic design quality of REST APIs for IoT applications and assesses their linguistic quality by performing the detection of linguistic patterns and antipatterns in REST APIs for IoT applications. Linguistic antipatterns are considered poor practices in the naming, documentation, and choice of identifiers. In contrast, linguistic patterns represent best practices to APIs design. The linguistic patterns and their corresponding antipatterns are hence contrasting pairs. We propose the SARAv2 (Semantic Analysis of REST APIs version two) approach to perform syntactic and semantic analyses of REST APIs for IoT applications. Based on the SARAv2 approach, we develop the REST-Ling tool and empirically validate the detection results of nine linguistic antipatterns. We analyse 19 REST APIs for IoT applications. Our detection results show that the linguistic antipatterns are prevalent and the REST-Ling tool can detect linguistic patterns and antipatterns in REST APIs for IoT applications with an average accuracy of over 80%. Moreover, the tool performs the detection of linguistic antipatterns on average in the order of seconds, i.e., 8.396 s. We found that APIs generally follow good linguistic practices, although the prevalence of poor practices exists. (C) 2022 The Author(s). Published by Elsevier Inc.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
REST APIs, IoT applications, Linguistic quality, Pattern, Antipattern, Detection
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-115189 (URN)10.1016/j.jss.2022.111369 (DOI)000814741100004 ()2-s2.0-85131218016 (Scopus ID)
Available from: 2022-07-06 Created: 2022-07-06 Last updated: 2023-04-06Bibliographically approved
Maleki, N., Musaddiq, A., Toll, D., Palma, F., Olsson, T., Mozart, D., . . . Ahlgren, F. (2022). DynaSens: Dynamic Scheduling for IoT Devices Sustainability. In: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022: . Paper presented at 4th International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2022, Graz12-14 July 2022. IEEE
Open this publication in new window or tab >>DynaSens: Dynamic Scheduling for IoT Devices Sustainability
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2022 (English)In: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. © 2022 IEEE.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Automation; Energy utilization; Intelligent buildings; Internet of things; Optical radar; Scheduling; Sustainable development; Waste management, Complex task; Core technology; Dynamic scheduling; Dynamic sensing; Energy-consumption; Large scale Internet; Potential benefits; Quality of life; Scheduling; Smart homes, Energy efficiency
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-118122 (URN)10.1109/CoBCom55489.2022.9880629 (DOI)000861442700004 ()2-s2.0-85139447481 (Scopus ID)9781665485982 (ISBN)
Conference
4th International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2022, Graz12-14 July 2022
Available from: 2023-01-03 Created: 2023-01-03 Last updated: 2023-11-02Bibliographically approved
Musaddiq, A., Maleki, N., Palma, F., Mozart, D., Olsson, T., Omareen, M. & Ahlgren, F. (2022). Internet of Things for Wetland Conservation using Helium Network: Experience and Analysis. In: 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022: . Paper presented at 12th International Conference on the Internet of Things, IoT 2022, Delft, 7 - 10 November 2022 (pp. 143-146). ACM Digital Library
Open this publication in new window or tab >>Internet of Things for Wetland Conservation using Helium Network: Experience and Analysis
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2022 (English)In: 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022, ACM Digital Library, 2022, p. 143-146Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.

Place, publisher, year, edition, pages
ACM Digital Library, 2022
Series
ACM International Conference Proceeding Series
Keywords
IoT, Helium, LoRa, Wetland
National Category
Communication Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer and Information Sciences Computer Science, Information Systems
Identifiers
urn:nbn:se:lnu:diva-118204 (URN)10.1145/3567445.3569167 (DOI)2-s2.0-85146605122 (Scopus ID)9781450396653 (ISBN)
Conference
12th International Conference on the Internet of Things, IoT 2022, Delft, 7 - 10 November 2022
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-11-02Bibliographically approved
Palma, F., Olsson, T., Wingkvist, A. & Ahlgren, F. (2022). Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs. In: Ardagna C.A., Bian H., Chang C.K., Chang R.N., Damiani E., Dustdar S., Marco J., Singh M., Teniente E., Ward R., Wang Z., Xhafa F., Zhang J. (Ed.), Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022: . Paper presented at IEEE International Conference on Services Computing, SCC 2022, Barcelona 10-16 July 2022 (pp. 20-30). IEEE
Open this publication in new window or tab >>Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs
2022 (English)In: Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022 / [ed] Ardagna C.A., Bian H., Chang C.K., Chang R.N., Damiani E., Dustdar S., Marco J., Singh M., Teniente E., Ward R., Wang Z., Xhafa F., Zhang J., IEEE, 2022, p. 20-30Conference paper, Published paper (Refereed)
Abstract [en]

Application Programming Interfaces (APIs) define how Web services, middle-wares, frameworks, and libraries communicate with their clients. An API that conforms to REpresentational State Transfer (REST) design principles is known as REST API. At present, it is an industry-standard for interaction among Web services. There exist mainly three categories of APIs: public, partner, and private. Public APIs are designed for external consumers, whereas partner APIs are designed aiming at organizational partners. In contrast, private APIs are designed solely for internal use. The API quality matters regardless of their category and intended consumers. To assess the (linguistic) design of APIs, researchers defined linguistic patterns (i.e., best API design practices) and linguistic antipatterns (i.e., poor API design practices.) APIs that follow linguistic patterns are easy to understand, use, and maintain. In this study, we analyze and compare the design quality of public, partner, and private APIs. More specifically, we made a large survey by analyzing and performing the detection of nine linguistic patterns and their corresponding antipatterns on more than 2,500 end-points from 37 APIs. Our results suggest that (1) public, partner, and private APIs lack quality linguistic design, (2) among the three API categories, private APIs lack linguistic design the most, and (3) end-points are amorphous, contextless, and non-descriptive in partner APIs. End-points have contextless design and poor documentation regardless of the API categories. 

Place, publisher, year, edition, pages
IEEE, 2022
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-118127 (URN)10.1109/SCC55611.2022.00017 (DOI)000858883900003 ()2-s2.0-85138013995 (Scopus ID)9781665481465 (ISBN)
Conference
IEEE International Conference on Services Computing, SCC 2022, Barcelona 10-16 July 2022
Available from: 2023-01-04 Created: 2023-01-04 Last updated: 2023-03-06Bibliographically approved
Zabardast, E., Gonzalez-Huerta, J. & Palma, F. (2022). The Impact of Forced Working-From-Home on Code Technical Debt: An Industrial Case Study. In: Callico G.M., Hebig R., Wortmann A. (Ed.), Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022: . Paper presented at Conference of 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022, 31 August-2 September 2022 (pp. 298-305). IEEE
Open this publication in new window or tab >>The Impact of Forced Working-From-Home on Code Technical Debt: An Industrial Case Study
2022 (English)In: Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022 / [ed] Callico G.M., Hebig R., Wortmann A., IEEE, 2022, p. 298-305Conference paper, Published paper (Refereed)
Abstract [en]

Background: The COVID-19 outbreak interrupted regular activities for over a year in many countries and resulted in a radical change in ways of working for software development companies, i.e., most software development companies switched to a forced Working-From-Home (WFH) mode.

Aim: Although several studies have analysed different aspects of forced WFH mode, it is unknown whether and to what extent WFH impacted the accumulation of technical debt (TD) when developers have different ways to coordinate and communicate with peers.

Method: Using the year 2019 as a baseline, we carried out an industrial case study to analyse the evolution of TD in five components that are part of a large project while WFH. As part of the data collection, we carried out a focus group with developers to explain the different patterns observed from the quantitative data analysis.

Results: TD accumulated at a slower pace during WFH as compared with the working-from-office period in four components out of five. These differences were found to be statistically significant. Through a focus group, we have identified different factors that might explain the changes in TD accumulation. One of these factors is responsibility diffusion which seems to explain why TD grows faster during the WFH period in one of the components. Conclusion: The results suggest that when the ways of working change, the change between working from office and working from home does not result in an increased accumulation of TD.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Software design, Case-studies; Data collection; Empirical studies; Focus groups; Industrial case study; Industrial study; Large programs; Technical debts; Telework; Work from home, COVID-19
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-122829 (URN)10.1109/SEAA56994.2022.00054 (DOI)2-s2.0-85142493452 (Scopus ID)9781665461528 (ISBN)
Conference
Conference of 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022, 31 August-2 September 2022
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-08-18Bibliographically approved
Eken, B., Palma, F., Ayse, B. & Ayse, T. (2021). An empirical study on the effect of community smells on bug prediction. Software quality journal, 29, 159-194
Open this publication in new window or tab >>An empirical study on the effect of community smells on bug prediction
2021 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 29, p. 159-194Article in journal (Refereed) Published
Abstract [en]

Community-aware metrics through socio-technical developer networks or organizational structures have already been studied in the software bug prediction field. Community smells are also proposed to identify communication and collaboration patterns in developer communities. Prior work reports a statistical association between community smells and code smells identified in software modules. We investigate the contribution of community smells on predicting bug-prone classes and compare their contribution with that of code smell-related information and state-of-the-art process metrics. We conduct our empirical analysis on ten open-source projects with varying sizes, buggy and smelly class ratios. We build seven different bug prediction models to answer three RQs: a baseline model including a state-of-the-art metric set used, three models incorporating a particular metric set, namely community smells, code smells, code smell intensity, into the baseline, and three models incorporating a combination of smell-related metrics into the baseline. The performance of these models is reported in terms of recall, false positive rates, F-measure and AUC and statistically compared using Scott-Knott ESD tests. Community smells improve the prediction performance of a baseline model by up to 3% in terms of AUC, while code smell intensity improves the baseline models by up to 40% in terms of F-measure and up to 17% in terms of AUC. The conclusions are significantly influenced by the validation strategies used, algorithms and the selected projects' data characteristics. While the code smell intensity metric captures the most information about technical flaws in predicting bug-prone classes, the community smells also contribute to bug prediction models by revealing communication and collaboration flaws in software development teams. Future research is needed to capture the communication patterns through multiple channels and to understand whether socio-technical flaws could be used in a cross-project bug prediction setting.

Place, publisher, year, edition, pages
Springer, 2021
Keywords
Community smells, Bug prediction, Mining software repositories
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-101509 (URN)10.1007/s11219-020-09538-7 (DOI)000618145600001 ()2-s2.0-85100871955 (Scopus ID)2021 (Local ID)2021 (Archive number)2021 (OAI)
Available from: 2021-03-05 Created: 2021-03-05 Last updated: 2022-05-17Bibliographically approved
Palma, F., Zarraa, O. & Sadia, A. (2021). Are Developers Equally Concerned About Making Their APIs RESTful and the Linguistic Quality?: A Study on Google APIs. In: Hakim Hacid;Odej Kao;Massimo Mecella;Naouel Moha;Hye-young Paik (Ed.), Service-Oriented Computing: 19th International Conference, ICSOC 2021, Virtual Event, November 22–25, 2021, Proceedings. Paper presented at ICSOC 2021, Online, November 22–25, 2021 (pp. 171-187). Springer
Open this publication in new window or tab >>Are Developers Equally Concerned About Making Their APIs RESTful and the Linguistic Quality?: A Study on Google APIs
2021 (English)In: Service-Oriented Computing: 19th International Conference, ICSOC 2021, Virtual Event, November 22–25, 2021, Proceedings / [ed] Hakim Hacid;Odej Kao;Massimo Mecella;Naouel Moha;Hye-young Paik, Springer, 2021, p. 171-187Conference paper, Published paper (Refereed)
Abstract [en]

REST (REpresentational State Transfer) is an architectural style for distributed, hypermedia systems that allows communication between clients and servers using the HTTP methods and URIs (Uniform Resource Identifiers). In the literature, researchers and practitioners defined best design practices, i.e., REST patterns, violation of which are known as REST antipatterns. Also, clients need to understand the use and purpose of APIs while consuming them. A set of best practices is defined in the literature for APIs to have a better linguistic design, i.e., linguistic patterns, violation of which are known as linguistic antipatterns. For API developers, it is challenging to ensure that their APIs are RESTful and manifest linguistic design quality. This paper investigates whether developers are equally concerned about making their APIs RESTful while also focus on designing APIs with better linguistic quality that may facilitate their comprehension and consumption. Thus, we examine the relation between RESTful and linguistic design quality in RESTful APIs. We analyzed eight Google APIs and performed the detection of 21 patterns and antipatterns on those APIs. Using the quantitative data, we performed a series of statistical tests. Results suggest a negligible relationship between RESTful and linguistic design quality. Thus, developers are unaware of whether they conjointly lack RESTful and linguistic design quality.

Place, publisher, year, edition, pages
Springer, 2021
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13121
Keywords
Antipatterns, Detection, Linguistic quality, Patterns, RESTful APIs, RESTful design, Uniform resource identifiers, Application programming interfaces (API), Hypermedia systems, Linguistics, Anti-patterns, Design Quality, Google+, Pattern, Representational state transfer, RESTful API, Pattern recognition
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-112584 (URN)10.1007/978-3-030-91431-8_11 (DOI)2-s2.0-85120535798 (Scopus ID)9783030914301 (ISBN)9783030914318 (ISBN)
Conference
ICSOC 2021, Online, November 22–25, 2021
Available from: 2022-05-06 Created: 2022-05-06 Last updated: 2024-08-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7092-2244

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