Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00013
Girish Chandra, Arunabha Mukhopadhyay
Recommender Systems are popular in domains such as Entertainment, Ecommerce, Social, Job however there are many new domains where it can play a useful role. In this Paper, Application of Recommender system is proposed in Standardization area to recommend set of Consortiums to Technology Organizations for membership. Consortiums (such as The Open Group, Object Management Group and others) specialize in one or more than one area(s) and active in Standardization. To formalize their work as International Standard, they Liaison with ISO Sub Committee(s)/Working Group(s) where they work with other Participants and after due process publish the work as formal International Standard. Due to Technological evolution, multiple such Consortiums are emerging as a Long Tail where the most popular Consortium gets more focus than others. In this Paper, the Proposed Recommendation Engine uses the Consortium Focus Area Attributes and membership details to recommend the most suitable set of Consortiums to Technology Organizations.
{"title":"Application of Recommender System in Standardization","authors":"Girish Chandra, Arunabha Mukhopadhyay","doi":"10.1109/CONISOFT52520.2021.00013","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00013","url":null,"abstract":"Recommender Systems are popular in domains such as Entertainment, Ecommerce, Social, Job however there are many new domains where it can play a useful role. In this Paper, Application of Recommender system is proposed in Standardization area to recommend set of Consortiums to Technology Organizations for membership. Consortiums (such as The Open Group, Object Management Group and others) specialize in one or more than one area(s) and active in Standardization. To formalize their work as International Standard, they Liaison with ISO Sub Committee(s)/Working Group(s) where they work with other Participants and after due process publish the work as formal International Standard. Due to Technological evolution, multiple such Consortiums are emerging as a Long Tail where the most popular Consortium gets more focus than others. In this Paper, the Proposed Recommendation Engine uses the Consortium Focus Area Attributes and membership details to recommend the most suitable set of Consortiums to Technology Organizations.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00017
Victor Contreras-Figueroa, L. G. Montané-Jiménez, T. Cepero, E. Benítez-Guerrero, Carmen Mezura-Godoy
Today there are smart cities that, through the use of information technologies, focus their efforts on improving the quality of life of their inhabitants by using sensors and specialized infrastructure. From these efforts arose the need to analyze and represent data within a system to make it useful, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and contexts. This article provides a systematic review of the literature on information visualization in adaptable dashboards. We present the visual components organized according to the information they can display and the identification of a procedure for the construction of dashboards. We proposed a component specification for adaptable dashboards, which integrates users, their information, interaction, and dashboard building guidelines to be integrated into smart city solutions.
{"title":"Information Visualization In Adaptable Dashboards For Smart Cities: A Systematic Review","authors":"Victor Contreras-Figueroa, L. G. Montané-Jiménez, T. Cepero, E. Benítez-Guerrero, Carmen Mezura-Godoy","doi":"10.1109/CONISOFT52520.2021.00017","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00017","url":null,"abstract":"Today there are smart cities that, through the use of information technologies, focus their efforts on improving the quality of life of their inhabitants by using sensors and specialized infrastructure. From these efforts arose the need to analyze and represent data within a system to make it useful, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and contexts. This article provides a systematic review of the literature on information visualization in adaptable dashboards. We present the visual components organized according to the information they can display and the identification of a procedure for the construction of dashboards. We proposed a component specification for adaptable dashboards, which integrates users, their information, interaction, and dashboard building guidelines to be integrated into smart city solutions.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117077567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00014
J. Cervantes-Ojeda, A. Badillo-Salas, M. Gómez-Fuentes
The User Interface Transition Diagram (UITD) is a graphic notation designed to simplify the specification and design of the system-user interactions without losing the technical detail that is necessary to develop the system. The UITD aims to be a good communication tool between customers and software developers. We present here a specialized graphic tool for editing User Interface Transition Diagrams: the UITD editor. It provides specialized functionalities to simplify the edition of UITD properties in comparison with existing graphic editing tools.
{"title":"Specialized Tool for Editing User Interface Transitions Diagrams (UITD)","authors":"J. Cervantes-Ojeda, A. Badillo-Salas, M. Gómez-Fuentes","doi":"10.1109/CONISOFT52520.2021.00014","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00014","url":null,"abstract":"The User Interface Transition Diagram (UITD) is a graphic notation designed to simplify the specification and design of the system-user interactions without losing the technical detail that is necessary to develop the system. The UITD aims to be a good communication tool between customers and software developers. We present here a specialized graphic tool for editing User Interface Transition Diagrams: the UITD editor. It provides specialized functionalities to simplify the edition of UITD properties in comparison with existing graphic editing tools.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123883036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00018
M. Gómez-Fuentes, J. Cervantes-Ojeda, A. García-Nájera
In this work we revisit the known problem of the lack of well-defined semantics, at the implementation level, for association and aggregation relationships in class diagrams to hypothesize that, in the context of software development, there is a better understanding of the associations between classes when omitting aggregation. We conducted an experimental study in which a questionnaire was applied to 100 subjects, 50 in the test group and 50 in the control group. The obtained responses were analyzed with statistical methods. From our results we conclude that it is not useful, from the point of view of software design, to differentiate between association and aggregation class relationships when a model will be implemented.
{"title":"Association and Aggregation Class Relationships: is there a Difference in Terms of Implementation?","authors":"M. Gómez-Fuentes, J. Cervantes-Ojeda, A. García-Nájera","doi":"10.1109/CONISOFT52520.2021.00018","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00018","url":null,"abstract":"In this work we revisit the known problem of the lack of well-defined semantics, at the implementation level, for association and aggregation relationships in class diagrams to hypothesize that, in the context of software development, there is a better understanding of the associations between classes when omitting aggregation. We conducted an experimental study in which a questionnaire was applied to 100 subjects, 50 in the test group and 50 in the control group. The obtained responses were analyzed with statistical methods. From our results we conclude that it is not useful, from the point of view of software design, to differentiate between association and aggregation class relationships when a model will be implemented.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134371626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00035
Muteb Alobaid, R. Ramachandran
Social media sites are becoming more popular places for exchanging information, and the amount of information available on social media has facilitated how people communicate with each other. One of the significant challenges for social media users is to deal with information overload, misinformation, disinformation, and fake news. Consequently, improving the skills of IDL and awareness of information context is one of the best ways for social media users to deal with information overload and identify fake news. However, the support of social media users is inconsistent which has led to many of them dealing poorly with misinformation and fake news.This research seeks to study and identify the impact on businesses and consumers of fake news and reviews on social media sites and seek to understand the role social media users play in combating fake news. Additionally, the study aims to understand the level of social media users' information and digital literacy skills. Our main results show that information overload, fake news, and reviews impact businesses and consumers.
{"title":"A Social Media Case Study on the Impact of Disinformation on Business and Consumers","authors":"Muteb Alobaid, R. Ramachandran","doi":"10.1109/CONISOFT52520.2021.00035","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00035","url":null,"abstract":"Social media sites are becoming more popular places for exchanging information, and the amount of information available on social media has facilitated how people communicate with each other. One of the significant challenges for social media users is to deal with information overload, misinformation, disinformation, and fake news. Consequently, improving the skills of IDL and awareness of information context is one of the best ways for social media users to deal with information overload and identify fake news. However, the support of social media users is inconsistent which has led to many of them dealing poorly with misinformation and fake news.This research seeks to study and identify the impact on businesses and consumers of fake news and reviews on social media sites and seek to understand the role social media users play in combating fake news. Additionally, the study aims to understand the level of social media users' information and digital literacy skills. Our main results show that information overload, fake news, and reviews impact businesses and consumers.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128934707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00038
Agustin-Daniel Ambrosio-Aguilar, E. Bárcenas, G. Molero-Castillo, Rocío Aldeco-Pérez
Tweet geolocation is very important in many contexts: disaster relief, opinion polling, recommendation systems, etc. There are some recent studies showing that tweets with geolocation tags are sparse in several settings. Current state of the art geolocation algorithms for tweets are based on natural language processing methods. Most of these algorithms have been tested in English.Transformers are machine learning models based on attention mechanisms. These models have been proven successful in many natural language processing and computer vision scenarios. In this paper, we propose a transformer model for tweet geolocation. We describe several experiments for tweets in Spanish located in the Mexican region.
{"title":"Geolocation of Tweets in Spanish with Transformer Encoders","authors":"Agustin-Daniel Ambrosio-Aguilar, E. Bárcenas, G. Molero-Castillo, Rocío Aldeco-Pérez","doi":"10.1109/CONISOFT52520.2021.00038","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00038","url":null,"abstract":"Tweet geolocation is very important in many contexts: disaster relief, opinion polling, recommendation systems, etc. There are some recent studies showing that tweets with geolocation tags are sparse in several settings. Current state of the art geolocation algorithms for tweets are based on natural language processing methods. Most of these algorithms have been tested in English.Transformers are machine learning models based on attention mechanisms. These models have been proven successful in many natural language processing and computer vision scenarios. In this paper, we propose a transformer model for tweet geolocation. We describe several experiments for tweets in Spanish located in the Mexican region.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133211771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00032
K. Phung, E. Ogunshile, M. Aydin
Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce sufficient information for developers and testers. In this paper, we explore a novel approach using a streamlined process linking Stream X-Machine and machine learning techniques to predict if software modules are prone to having a particular type of runtime error in Java programs. In particular, Stream X-Machine is used to model and generate test cases for different types of Java runtime errors, which will be employed to extract error-type data from the source codes. This data is subsequently added to the collected software metrics to form new training data sets. We then explore the capabilities of three machine learning techniques (Support Vector Machine, Decision Tree, and Multi-layer Perceptron) for error-type proneness prediction. The experimental results showed that the new data sets could significantly improve the performances of machine learning models in terms of predicting error-type proneness.
{"title":"A Novel Software Fault Prediction Approach To Predict Error-type Proneness in the Java Programs Using Stream X-Machine and Machine Learning","authors":"K. Phung, E. Ogunshile, M. Aydin","doi":"10.1109/CONISOFT52520.2021.00032","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00032","url":null,"abstract":"Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce sufficient information for developers and testers. In this paper, we explore a novel approach using a streamlined process linking Stream X-Machine and machine learning techniques to predict if software modules are prone to having a particular type of runtime error in Java programs. In particular, Stream X-Machine is used to model and generate test cases for different types of Java runtime errors, which will be employed to extract error-type data from the source codes. This data is subsequently added to the collected software metrics to form new training data sets. We then explore the capabilities of three machine learning techniques (Support Vector Machine, Decision Tree, and Multi-layer Perceptron) for error-type proneness prediction. The experimental results showed that the new data sets could significantly improve the performances of machine learning models in terms of predicting error-type proneness.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00030
Delmer Alejandro López-Hernández, Jorge Octavio Ocharán-Hernández, E. Mezura-Montes, Á. Sánchez-García
Software requirements classification is a human-intensive task performed during the requirements analysis phase in software development. This literature review analyzes the state-of-the-art of the classification of software requirements using Artificial Neural Networks. Fourteen articles were selected to conduct the review. Sixteen different techniques to classify requirements were identified where, besides artificial neural networks, the most popular are Naive Bayes and the Support Vector Machine. Among the reported Artificial Neural Networks, we identify Convolutional Neural Networks and a Shallow Neural Network. We also found seven approaches that classify functional and non-functional requirements, six that classify only non-functional requirements, and one of them that classifies only functional requirements. The most used metrics to express classification results were accuracy, recall, and F-score. Finally, the results of the classifiers are gathered and reported.
{"title":"Automatic Classification of Software Requirements using Artificial Neural Networks: A Systematic Literature Review","authors":"Delmer Alejandro López-Hernández, Jorge Octavio Ocharán-Hernández, E. Mezura-Montes, Á. Sánchez-García","doi":"10.1109/CONISOFT52520.2021.00030","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00030","url":null,"abstract":"Software requirements classification is a human-intensive task performed during the requirements analysis phase in software development. This literature review analyzes the state-of-the-art of the classification of software requirements using Artificial Neural Networks. Fourteen articles were selected to conduct the review. Sixteen different techniques to classify requirements were identified where, besides artificial neural networks, the most popular are Naive Bayes and the Support Vector Machine. Among the reported Artificial Neural Networks, we identify Convolutional Neural Networks and a Shallow Neural Network. We also found seven approaches that classify functional and non-functional requirements, six that classify only non-functional requirements, and one of them that classifies only functional requirements. The most used metrics to express classification results were accuracy, recall, and F-score. Finally, the results of the classifiers are gathered and reported.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121610649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00019
Saarayim González-Hemández, Á. Sánchez-García, K. Cortés-Verdín, J. C. Pérez-Arriaga
Software estimation is a fundamental activity in the Software development process, since it is possible to predict the number of defects, size, effort, among other attributes. With this, it is possible to improve the quality of the product and process. To predict quantitative values, it is common to use Regression Model mechanisms, although each model adjusts to a specific behavior of the data. In this work, a Systematic Literature Review is carried out based on the Kitchenham and Charters guide, to know the different types of Regression that have been used in the Software estimates. In addition, it seeks to know those attributes that are estimated and those that function as independent variables. Simple Linear Regression, Multiple Linear Regression and Logistic Regression were the most used, although other types of regression were found that can be further explored. Finally, the attributes that work as predictor variables were categorized, where the attributes of effort, lines of code and use cases were the most frequent.
{"title":"Regression in Estimation of Software Attributes: A Systematic Literature Review","authors":"Saarayim González-Hemández, Á. Sánchez-García, K. Cortés-Verdín, J. C. Pérez-Arriaga","doi":"10.1109/CONISOFT52520.2021.00019","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00019","url":null,"abstract":"Software estimation is a fundamental activity in the Software development process, since it is possible to predict the number of defects, size, effort, among other attributes. With this, it is possible to improve the quality of the product and process. To predict quantitative values, it is common to use Regression Model mechanisms, although each model adjusts to a specific behavior of the data. In this work, a Systematic Literature Review is carried out based on the Kitchenham and Charters guide, to know the different types of Regression that have been used in the Software estimates. In addition, it seeks to know those attributes that are estimated and those that function as independent variables. Simple Linear Regression, Multiple Linear Regression and Logistic Regression were the most used, although other types of regression were found that can be further explored. Finally, the attributes that work as predictor variables were categorized, where the attributes of effort, lines of code and use cases were the most frequent.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127032746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CONISOFT52520.2021.00016
V. M. Niño-Martínez, Jorge Octavio Ocharán-Hernández, X. Limón, J. C. Pérez-Arriaga
The microservices architecture is a set of small, autonomous services that can work together to form a single application that has seen widespread adoption by practitioners. However, deploying this architecture creates several technical challenges. This study examines the state of the art of microservices deployment in the literature, informing researchers and practitioners about the techniques and technologies used in the deployment of microservices and, practices used in the DevOps culture. We conducted a systematic mapping study driven by four research questions related to the deployment of microservices and DevOps practices, and 21 studies were identified from which information was synthesized using the meta-aggregation method. With the information synthesis, 43 findings were extracted and classified into seven categories. We can summarize the findings of this study as follows: (i) We identified the essential DevOps practices in the deployment of microservices. (ii) We defined the stack of technologies with the highest incidence in the studies. (iii) We found three groups of challenges in microservices deployment. (iv) Finally, we present a set of recommendations for microservices deployment.
{"title":"Microservices Deployment: A Systematic Mapping Study","authors":"V. M. Niño-Martínez, Jorge Octavio Ocharán-Hernández, X. Limón, J. C. Pérez-Arriaga","doi":"10.1109/CONISOFT52520.2021.00016","DOIUrl":"https://doi.org/10.1109/CONISOFT52520.2021.00016","url":null,"abstract":"The microservices architecture is a set of small, autonomous services that can work together to form a single application that has seen widespread adoption by practitioners. However, deploying this architecture creates several technical challenges. This study examines the state of the art of microservices deployment in the literature, informing researchers and practitioners about the techniques and technologies used in the deployment of microservices and, practices used in the DevOps culture. We conducted a systematic mapping study driven by four research questions related to the deployment of microservices and DevOps practices, and 21 studies were identified from which information was synthesized using the meta-aggregation method. With the information synthesis, 43 findings were extracted and classified into seven categories. We can summarize the findings of this study as follows: (i) We identified the essential DevOps practices in the deployment of microservices. (ii) We defined the stack of technologies with the highest incidence in the studies. (iii) We found three groups of challenges in microservices deployment. (iv) Finally, we present a set of recommendations for microservices deployment.","PeriodicalId":380632,"journal":{"name":"2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117203974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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