Pub Date : 2021-09-01DOI: 10.1109/SEAA53835.2021.00021
Andrea Fiore, Alfonso Russo, C. Gravino, M. Risi
The paper focuses on Cross-Version Defect Prediction (CVDP) where the classification model is trained on information of the prior version and then tested to predict defects in the components of the last release. To avoid the distribution differences which could negatively impact the performances of machine learning based model, we consider Dissimilarity-based Sparse Subset Selection (DS3) technique for selecting meaningful representatives to be included in the training set. Furthermore, we employ a Convolutional Neural Network (CNN) to generate structural and semantic features to be merged with the traditional software measures to obtain a more comprehensive list of predictors. To evaluate the usefulness of our proposal for the CVDP scenario, we perform an empirical study on a total of 20 cross-version pairs from 10 different software projects. To build prediction models we consider Logistic Regression (LR) and Random Forest (RF) and we adopt 3 evaluation criteria (i.e., F-measure, G-mean, Balance) to assess the prediction accuracy. Our results show that the use of CNN with both LR and RF models has a significant impact, with an improvement of ∼20% for each evaluation criteria. Differently, we notice that DS3 does not impact significantly in improving prediction accuracy.
{"title":"Combining CNN with DS3 for Detecting Bug-prone Modules in Cross-version Projects","authors":"Andrea Fiore, Alfonso Russo, C. Gravino, M. Risi","doi":"10.1109/SEAA53835.2021.00021","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00021","url":null,"abstract":"The paper focuses on Cross-Version Defect Prediction (CVDP) where the classification model is trained on information of the prior version and then tested to predict defects in the components of the last release. To avoid the distribution differences which could negatively impact the performances of machine learning based model, we consider Dissimilarity-based Sparse Subset Selection (DS3) technique for selecting meaningful representatives to be included in the training set. Furthermore, we employ a Convolutional Neural Network (CNN) to generate structural and semantic features to be merged with the traditional software measures to obtain a more comprehensive list of predictors. To evaluate the usefulness of our proposal for the CVDP scenario, we perform an empirical study on a total of 20 cross-version pairs from 10 different software projects. To build prediction models we consider Logistic Regression (LR) and Random Forest (RF) and we adopt 3 evaluation criteria (i.e., F-measure, G-mean, Balance) to assess the prediction accuracy. Our results show that the use of CNN with both LR and RF models has a significant impact, with an improvement of ∼20% for each evaluation criteria. Differently, we notice that DS3 does not impact significantly in improving prediction accuracy.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681466","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00047
Hongyi Zhang, J. Bosch, H. H. Olsson, Ashok Chaitanya Koppisetty
In recent years, with more edge devices being put into use, the amount of data that is created, transmitted and stored is increasing exponentially. Moreover, due to the development of machine learning algorithms, modern software-intensive systems are able to take advantage of the data to further improve their service quality. However, it is expensive and inefficient to transmit large amounts of data to a central location for the purpose of training and deploying machine learning models. Data transfer from edge devices across the globe to central locations may also raise privacy and concerns related to local data regulations. As a distributed learning approach, Federated Learning has been introduced to tackle those challenges. Since Federated Learning simply exchanges locally trained machine learning models rather than the entire data set throughout the training process, the method not only protects user data privacy but also improves model training efficiency. In this paper, we have investigated an advanced machine learning algorithm, Deep Neural Decision Forests (DNDF), which unites classification trees with the representation learning functionality from deep convolutional neural networks. In this paper, we propose a novel algorithm, AF-DNDF which extends DNDF with an asynchronous federated aggregation protocol. Based on the local quality of each classification tree, our architecture can select and combine the optimal groups of decision trees from multiple local devices. The introduction of the asynchronous protocol enables the algorithm to be deployed in the industrial context with heterogeneous hardware settings. Our AF-DNDF architecture is validated in an automotive industrial use case focusing on road objects recognition and demonstrated by an empirical experiment with two different data sets. The experimental results show that our AF-DNDF algorithm significantly reduces the communication overhead and accelerates model training speed without sacrificing model classification performance. The algorithm can reach the same classification accuracy as the commonly used centralized machine learning methods but also greatly improve local edge model quality.
{"title":"AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests","authors":"Hongyi Zhang, J. Bosch, H. H. Olsson, Ashok Chaitanya Koppisetty","doi":"10.1109/SEAA53835.2021.00047","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00047","url":null,"abstract":"In recent years, with more edge devices being put into use, the amount of data that is created, transmitted and stored is increasing exponentially. Moreover, due to the development of machine learning algorithms, modern software-intensive systems are able to take advantage of the data to further improve their service quality. However, it is expensive and inefficient to transmit large amounts of data to a central location for the purpose of training and deploying machine learning models. Data transfer from edge devices across the globe to central locations may also raise privacy and concerns related to local data regulations. As a distributed learning approach, Federated Learning has been introduced to tackle those challenges. Since Federated Learning simply exchanges locally trained machine learning models rather than the entire data set throughout the training process, the method not only protects user data privacy but also improves model training efficiency. In this paper, we have investigated an advanced machine learning algorithm, Deep Neural Decision Forests (DNDF), which unites classification trees with the representation learning functionality from deep convolutional neural networks. In this paper, we propose a novel algorithm, AF-DNDF which extends DNDF with an asynchronous federated aggregation protocol. Based on the local quality of each classification tree, our architecture can select and combine the optimal groups of decision trees from multiple local devices. The introduction of the asynchronous protocol enables the algorithm to be deployed in the industrial context with heterogeneous hardware settings. Our AF-DNDF architecture is validated in an automotive industrial use case focusing on road objects recognition and demonstrated by an empirical experiment with two different data sets. The experimental results show that our AF-DNDF algorithm significantly reduces the communication overhead and accelerates model training speed without sacrificing model classification performance. The algorithm can reach the same classification accuracy as the commonly used centralized machine learning methods but also greatly improve local edge model quality.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130337742","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00032
Valentina Lenarduzzi, A. Martini, Nyyti Saarimäki, D. Tamburri
Background: Technical Debt is a consolidated notion in software engineering research and practice. However, the estimation of its impact (interest of the debt) is still imprecise and requires heavy empirical and experimental inquiry. Objective: We aim at developing a data-driven approach to calculate the interest of Technical Debt in terms of delays in resolving affected tasks.Method: We conducted a case study to estimate the Technical Debt interest by analyzing its association with the lead time variation of resolving related Jira issues.Results: Data-driven approaches could significantly change the Technical Debt estimation and improve the removing Technical Debt prioritization. Our case study shows that the presence of Code Technical Debt did not affect the lead time for resolving the issues.Conclusion: Future works include the further refinement of this approach and its application to a larger data-set and on different type of issues.
{"title":"Technical Debt Impacting Lead-Times: An Exploratory Study","authors":"Valentina Lenarduzzi, A. Martini, Nyyti Saarimäki, D. Tamburri","doi":"10.1109/SEAA53835.2021.00032","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00032","url":null,"abstract":"Background: Technical Debt is a consolidated notion in software engineering research and practice. However, the estimation of its impact (interest of the debt) is still imprecise and requires heavy empirical and experimental inquiry. Objective: We aim at developing a data-driven approach to calculate the interest of Technical Debt in terms of delays in resolving affected tasks.Method: We conducted a case study to estimate the Technical Debt interest by analyzing its association with the lead time variation of resolving related Jira issues.Results: Data-driven approaches could significantly change the Technical Debt estimation and improve the removing Technical Debt prioritization. Our case study shows that the presence of Code Technical Debt did not affect the lead time for resolving the issues.Conclusion: Future works include the further refinement of this approach and its application to a larger data-set and on different type of issues.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114877327","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00013
Hugo Villamizar, Tatiana Escovedo, Marcos Kalinowski
Machine learning (ML) has become a core feature for today’s real-world applications, making it a trending topic for the software engineering community. Requirements Engineering (RE) is no stranger to this and its main conferences have included workshops aiming at discussing RE in the context of ML. However, current research on the intersection between RE and ML mainly focuses on using ML techniques to support RE activities rather than on exploring how RE can improve the development of ML-based systems. This paper concerns a systematic mapping study aiming at characterizing the publication landscape of RE for ML-based systems, outlining research contributions and contemporary gaps for future research. In total, we identified 35 studies that met our inclusion criteria. We found several different types of contributions, in the form of analyses, approaches, checklists and guidelines, quality models, and taxonomies. We discuss gaps by mapping these contributions against the RE topics to which they were contributing and their type of empirical evaluation. We also identified quality characteristics that are particularly relevant for the ML context (e.g., data quality, explainability, fairness, safety, and transparency). Main reported challenges are related to the lack of validated RE techniques, the fragmented and incomplete understanding of NFRs for ML, and difficulties in handling customer expectations. There is a need for future research on the topic to reveal best practices and to propose and investigate approaches that are suitable to be used in practice.
{"title":"Requirements Engineering for Machine Learning: A Systematic Mapping Study","authors":"Hugo Villamizar, Tatiana Escovedo, Marcos Kalinowski","doi":"10.1109/SEAA53835.2021.00013","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00013","url":null,"abstract":"Machine learning (ML) has become a core feature for today’s real-world applications, making it a trending topic for the software engineering community. Requirements Engineering (RE) is no stranger to this and its main conferences have included workshops aiming at discussing RE in the context of ML. However, current research on the intersection between RE and ML mainly focuses on using ML techniques to support RE activities rather than on exploring how RE can improve the development of ML-based systems. This paper concerns a systematic mapping study aiming at characterizing the publication landscape of RE for ML-based systems, outlining research contributions and contemporary gaps for future research. In total, we identified 35 studies that met our inclusion criteria. We found several different types of contributions, in the form of analyses, approaches, checklists and guidelines, quality models, and taxonomies. We discuss gaps by mapping these contributions against the RE topics to which they were contributing and their type of empirical evaluation. We also identified quality characteristics that are particularly relevant for the ML context (e.g., data quality, explainability, fairness, safety, and transparency). Main reported challenges are related to the lack of validated RE techniques, the fragmented and incomplete understanding of NFRs for ML, and difficulties in handling customer expectations. There is a need for future research on the topic to reveal best practices and to propose and investigate approaches that are suitable to be used in practice.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127954301","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00043
S. Counsell, R. Hierons, Krishna Patel
The 80-20 rule is well-known in the real-world. When applied to bugs, it suggests that 80% of bugs arise in just 20% of classes. One research question that has yet to be explored is whether the same rule applies to refactoring activity. In other words, do 20% of classes account for 80% of refactorings applied to a system? In this short paper, we explore this question using data from seven open-source systems drawn from two previous studies. In each case, we explore whether the 80-20 rule applies and suggest why. Results showed limited evidence of an 80-20 rule; in the two systems where it was evident, the refactoring profile implied firstly, a large-scale movement of class fields and methods and, secondly, the deliberate aim of collapsing the class hierarchy using inheritance-based refactorings.
{"title":"Are 20% of Classes Responsible for 80% of Refactorings?","authors":"S. Counsell, R. Hierons, Krishna Patel","doi":"10.1109/SEAA53835.2021.00043","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00043","url":null,"abstract":"The 80-20 rule is well-known in the real-world. When applied to bugs, it suggests that 80% of bugs arise in just 20% of classes. One research question that has yet to be explored is whether the same rule applies to refactoring activity. In other words, do 20% of classes account for 80% of refactorings applied to a system? In this short paper, we explore this question using data from seven open-source systems drawn from two previous studies. In each case, we explore whether the 80-20 rule applies and suggest why. Results showed limited evidence of an 80-20 rule; in the two systems where it was evident, the refactoring profile implied firstly, a large-scale movement of class fields and methods and, secondly, the deliberate aim of collapsing the class hierarchy using inheritance-based refactorings.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127886583","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00044
Gabriella Andrade, Dalvan Griebler, R. Santos, M. Danelutto, L. G. Fernandes
From the popularization of multi-core architectures, several parallel APIs have emerged, helping to abstract the programming complexity and increasing productivity in application development. Unfortunately, only a few research efforts in this direction managed to show the usability pay-back of the programming abstraction created, because it is not easy and poses many challenges for conducting empirical software engineering. We believe that coding metrics commonly used in software engineering code measurements can give useful indicators on the programming effort of parallel applications and APIs. These metrics were designed for general purposes without considering the evaluation of applications from a specific domain. In this study, we aim to evaluate the feasibility of seven coding metrics to be used in the parallel programming domain. To do so, five stream processing applications implemented with different parallel APIs for multi-cores were considered. Our experiments have shown COCOMO II is a suitable model for evaluating the productivity of different parallel APIs targeting multi-cores on stream processing applications while other metrics are restricted to the code size.
{"title":"Assessing Coding Metrics for Parallel Programming of Stream Processing Programs on Multi-cores","authors":"Gabriella Andrade, Dalvan Griebler, R. Santos, M. Danelutto, L. G. Fernandes","doi":"10.1109/SEAA53835.2021.00044","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00044","url":null,"abstract":"From the popularization of multi-core architectures, several parallel APIs have emerged, helping to abstract the programming complexity and increasing productivity in application development. Unfortunately, only a few research efforts in this direction managed to show the usability pay-back of the programming abstraction created, because it is not easy and poses many challenges for conducting empirical software engineering. We believe that coding metrics commonly used in software engineering code measurements can give useful indicators on the programming effort of parallel applications and APIs. These metrics were designed for general purposes without considering the evaluation of applications from a specific domain. In this study, we aim to evaluate the feasibility of seven coding metrics to be used in the parallel programming domain. To do so, five stream processing applications implemented with different parallel APIs for multi-cores were considered. Our experiments have shown COCOMO II is a suitable model for evaluating the productivity of different parallel APIs targeting multi-cores on stream processing applications while other metrics are restricted to the code size.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130153398","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00041
Sofie Hafstrøm Kristensen, M. Paasivaara
With an increasing number of companies choosing to implement Scrum, the role of the Scrum Master seems to change from how it is described in the Scrum Guide. Companies have challenges in hiring skilled Scrum Masters, and in seeing the value of this role after the agile adoption phase is over. This paper aspires to understand the value of Scrum masters by investigating the role in a case company, Nordea, while studying their experiment of assigning a single Scrum Master to multiple teams. We collected data by eleven semi-structured interviews and six observation sessions. Our findings show that the real value of a Scrum Master is based on his or her ability to understand people. The Scrum Master contributes to team dynamics and well-being, helps the team to connect and coordinate, and challenges the team to higher performance. Finally, assigning an experienced Scrum Master to two teams can lead to improved knowledge-sharing, cooperation and alignment. Assigning more than two teams to a single Scrum master may put the value this role provides at risk.
{"title":"What Added Value Does a Scrum Master Bring to the Organisation? — A Case Study at Nordea","authors":"Sofie Hafstrøm Kristensen, M. Paasivaara","doi":"10.1109/SEAA53835.2021.00041","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00041","url":null,"abstract":"With an increasing number of companies choosing to implement Scrum, the role of the Scrum Master seems to change from how it is described in the Scrum Guide. Companies have challenges in hiring skilled Scrum Masters, and in seeing the value of this role after the agile adoption phase is over. This paper aspires to understand the value of Scrum masters by investigating the role in a case company, Nordea, while studying their experiment of assigning a single Scrum Master to multiple teams. We collected data by eleven semi-structured interviews and six observation sessions. Our findings show that the real value of a Scrum Master is based on his or her ability to understand people. The Scrum Master contributes to team dynamics and well-being, helps the team to connect and coordinate, and challenges the team to higher performance. Finally, assigning an experienced Scrum Master to two teams can lead to improved knowledge-sharing, cooperation and alignment. Assigning more than two teams to a single Scrum master may put the value this role provides at risk.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127810929","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00051
Florian Auer, M. Felderer
Available platforms for online controlled experimentation primarily focus on the technical execution of experiments and are isolated from the remaining software development infrastructure. The platform-independent experimentation infrastructure separates the experiment definition from its execution and focuses on the experimentation process. However, it is still not integrated into the remaining infrastructure. In this paper, we extend the platform-independent experimentation infrastructure about interfaces to ease its integration into the software development infrastructure. The proposed solution is evaluated using a mixed-method research design to assess its usefulness, ease of use, strengths, and weaknesses. The results indicate that the proposed solution represents an adaptable, platform-independent, and cross-domain experimentation infrastructure that is perceived to be easy to use and useful.
{"title":"An Architecture to Integrate Experimentation into the Software Development Infrastructure","authors":"Florian Auer, M. Felderer","doi":"10.1109/SEAA53835.2021.00051","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00051","url":null,"abstract":"Available platforms for online controlled experimentation primarily focus on the technical execution of experiments and are isolated from the remaining software development infrastructure. The platform-independent experimentation infrastructure separates the experiment definition from its execution and focuses on the experimentation process. However, it is still not integrated into the remaining infrastructure. In this paper, we extend the platform-independent experimentation infrastructure about interfaces to ease its integration into the software development infrastructure. The proposed solution is evaluated using a mixed-method research design to assess its usefulness, ease of use, strengths, and weaknesses. The results indicate that the proposed solution represents an adaptable, platform-independent, and cross-domain experimentation infrastructure that is perceived to be easy to use and useful.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125344255","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00039
Ioannis Apatsidis, Konstantinos Georgiou, N. Mittas, L. Angelis
As the industry is moving towards digitalized solutions and practices, a growth in remote working has been observed with companies embracing flexibility for their workforce. Global crises, such as the coronavirus pandemic, have also accelerated this process, transforming the labor market. This trend is reflected in job portals, that contain an increasing number of remote job advertisements. Recognizing this evolving change, we perform a thorough study in Stack Overflow, to examine the main characteristics of remote working that discriminate it from its on-site counterpart. By collecting and analyzing 8514 job posts and leveraging text mining and graph theory methodologies, we attempt to pinpoint the primary elements that define each category, from dominant technologies to job positions and top seeking industries. The findings suggest that remote working presents differences from traditional working, being mainly associated with the software engineering sector and with well-known software development and data analytics technologies.
{"title":"A Study of Remote and On-site ICT Labor Market Demand using Job Offers from Stack Overflow","authors":"Ioannis Apatsidis, Konstantinos Georgiou, N. Mittas, L. Angelis","doi":"10.1109/SEAA53835.2021.00039","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00039","url":null,"abstract":"As the industry is moving towards digitalized solutions and practices, a growth in remote working has been observed with companies embracing flexibility for their workforce. Global crises, such as the coronavirus pandemic, have also accelerated this process, transforming the labor market. This trend is reflected in job portals, that contain an increasing number of remote job advertisements. Recognizing this evolving change, we perform a thorough study in Stack Overflow, to examine the main characteristics of remote working that discriminate it from its on-site counterpart. By collecting and analyzing 8514 job posts and leveraging text mining and graph theory methodologies, we attempt to pinpoint the primary elements that define each category, from dominant technologies to job positions and top seeking industries. The findings suggest that remote working presents differences from traditional working, being mainly associated with the software engineering sector and with well-known software development and data analytics technologies.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116574835","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 : 2021-09-01DOI: 10.1109/SEAA53835.2021.00038
Hüseyin Unlu, Samet Tenekeci, Ali Yildiz, Onur Demirörs
The rapidly developing internet infrastructure together with the advances in software technology has enabled the development of cloud-based modern web applications that are much more responsive, flexible, and reliable compared to traditional monolithic applications. Such modern applications require new software design paradigms and architectures. Microservice-based architecture (MSbA), which aims to create small, isolated, loosely-coupled applications that work in cohesion, becoming widespread as one of these approaches. MSbA allows the developed applications to be deployed and maintained separately, as well as scaled on demand. However, there is no de facto method for the analysis and design of systems for these architectures. In this paper, we compared the usefulness of the object-oriented (OO) and event-oriented (EO) approaches for analyzing and designing MS-based systems. More specifically, we performed an exploratory case study to analyze, design, and implement a software application dealing with the ‘application and evaluation process of graduate students at IzTech’. This paper discusses the results of this case study. We observe that the EO approaches have significant advantages with respect to the OO approaches.
{"title":"Event Oriented vs Object Oriented Analysis for Microservice Architecture: An Exploratory Case Study","authors":"Hüseyin Unlu, Samet Tenekeci, Ali Yildiz, Onur Demirörs","doi":"10.1109/SEAA53835.2021.00038","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00038","url":null,"abstract":"The rapidly developing internet infrastructure together with the advances in software technology has enabled the development of cloud-based modern web applications that are much more responsive, flexible, and reliable compared to traditional monolithic applications. Such modern applications require new software design paradigms and architectures. Microservice-based architecture (MSbA), which aims to create small, isolated, loosely-coupled applications that work in cohesion, becoming widespread as one of these approaches. MSbA allows the developed applications to be deployed and maintained separately, as well as scaled on demand. However, there is no de facto method for the analysis and design of systems for these architectures. In this paper, we compared the usefulness of the object-oriented (OO) and event-oriented (EO) approaches for analyzing and designing MS-based systems. More specifically, we performed an exploratory case study to analyze, design, and implement a software application dealing with the ‘application and evaluation process of graduate students at IzTech’. This paper discusses the results of this case study. We observe that the EO approaches have significant advantages with respect to the OO approaches.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127707695","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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