Pub Date : 2021-09-01DOI: 10.1109/SEAA53835.2021.00009
Vinicius dos Santos, É. Souza, K. Felizardo, W. Watanabe, Arnaldo Cândido Júnior, S. Aluísio, N. Vijaykumar
Context: Secondary studies, as Systematic Literature Reviews (SLRs) and Systematic Mappings (SMs), have been providing methodological and structured processes to identify and select research evidence in Computer Science, especially in Software Engineering (SE). One of the main activities of a secondary study process is to read the abstracts to decide on including or excluding studies. This activity is considered costly and time-consuming. In order to speed up the selection activity, some alternatives such as, structured abstracts and graphical abstracts (e.g. Concept Maps – CMs), have been proposed. Objective: This study presents an approach to automatically build CMs using Natural Language Processing (NLP) to support the selection activity of secondary studies. Method: First, we proposed an approach composed by two pipelines: (1) perform the triple extraction of concept-relation-concept based on NLP; and (2) attach the extracted triples in a structure used as a template to scientific studies. Second, we evaluated both pipelines conducting experiments. Results: The preliminary evaluation revealed that CMs extracted are coherent when compared with their source text. Conclusions: NLP can assist the automatic construction of CMs. In addition, the experiment results show that the approach can be useful to support researchers in the selection of studies in the selection activity of secondary studies.
{"title":"Using Natural Language Processing to Build Graphical Abstracts to be used in Studies Selection Activity in Secondary Studies","authors":"Vinicius dos Santos, É. Souza, K. Felizardo, W. Watanabe, Arnaldo Cândido Júnior, S. Aluísio, N. Vijaykumar","doi":"10.1109/SEAA53835.2021.00009","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00009","url":null,"abstract":"Context: Secondary studies, as Systematic Literature Reviews (SLRs) and Systematic Mappings (SMs), have been providing methodological and structured processes to identify and select research evidence in Computer Science, especially in Software Engineering (SE). One of the main activities of a secondary study process is to read the abstracts to decide on including or excluding studies. This activity is considered costly and time-consuming. In order to speed up the selection activity, some alternatives such as, structured abstracts and graphical abstracts (e.g. Concept Maps – CMs), have been proposed. Objective: This study presents an approach to automatically build CMs using Natural Language Processing (NLP) to support the selection activity of secondary studies. Method: First, we proposed an approach composed by two pipelines: (1) perform the triple extraction of concept-relation-concept based on NLP; and (2) attach the extracted triples in a structure used as a template to scientific studies. Second, we evaluated both pipelines conducting experiments. Results: The preliminary evaluation revealed that CMs extracted are coherent when compared with their source text. Conclusions: NLP can assist the automatic construction of CMs. In addition, the experiment results show that the approach can be useful to support researchers in the selection of studies in the selection activity of secondary studies.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"13 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":"125524255","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/SEAA53835.2021.00048
Jens Henriksson, C. Berger, Stig Ursing
Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory inputs through perception and planning to vehicle control of acceleration, breaking and steering. YOLO (you-only-look-once) is a state-of-the-art perception neural network (NN) architecture providing object detection and classification through bounding box estimations on camera images. As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set. We compare regular pedestrian detection to upper and lower body detection. Our findings show that the two NN using only partial information perform similarly well like the NN for the full body when the full body NN’s performance is 0.75 or better. Furthermore and as expected, the network, which is only trained on the lower half body is least prone to disturbances from occlusions of the upper half and vice versa.
{"title":"Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems","authors":"Jens Henriksson, C. Berger, Stig Ursing","doi":"10.1109/SEAA53835.2021.00048","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00048","url":null,"abstract":"Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory inputs through perception and planning to vehicle control of acceleration, breaking and steering. YOLO (you-only-look-once) is a state-of-the-art perception neural network (NN) architecture providing object detection and classification through bounding box estimations on camera images. As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set. We compare regular pedestrian detection to upper and lower body detection. Our findings show that the two NN using only partial information perform similarly well like the NN for the full body when the full body NN’s performance is 0.75 or better. Furthermore and as expected, the network, which is only trained on the lower half body is least prone to disturbances from occlusions of the upper half and vice versa.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"10 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":"128035638","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/SEAA53835.2021.00028
Lukas Nagel, Oliver Karras, J. Klünder
Software engineers in modern development settings often face the challenge of contributing to large existing projects. The comprehension of foreign software code presents a time consuming obstacle, especially in contexts like onboarding. New employees have little knowledge of the software project they are supposed to contribute to. Therefore, tools supporting developers with their code comprehension are desirable to help them contribute to the best of their ability as soon as possible. Such tools must be flexible enough to work with any software project, while offering means for adjustments to very specific tasks.In this paper, we present an approach to visualize source code as node-link diagrams, using expert-designed ontologies to group various source code elements such as classes or methods. We evaluate our approach with nine advanced computer science students simulating an onboarding in a software project consisting of almost 100k lines of code. The results show that our approach supports code comprehension by utilizing expert knowledge of the visualized project, while also pointing to other use cases such as legacy code migration.
{"title":"Ontology-Based Software Graphs for Supporting Code Comprehension During Onboarding","authors":"Lukas Nagel, Oliver Karras, J. Klünder","doi":"10.1109/SEAA53835.2021.00028","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00028","url":null,"abstract":"Software engineers in modern development settings often face the challenge of contributing to large existing projects. The comprehension of foreign software code presents a time consuming obstacle, especially in contexts like onboarding. New employees have little knowledge of the software project they are supposed to contribute to. Therefore, tools supporting developers with their code comprehension are desirable to help them contribute to the best of their ability as soon as possible. Such tools must be flexible enough to work with any software project, while offering means for adjustments to very specific tasks.In this paper, we present an approach to visualize source code as node-link diagrams, using expert-designed ontologies to group various source code elements such as classes or methods. We evaluate our approach with nine advanced computer science students simulating an onboarding in a software project consisting of almost 100k lines of code. The results show that our approach supports code comprehension by utilizing expert knowledge of the visualized project, while also pointing to other use cases such as legacy code migration.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"81 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976527","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/SEAA53835.2021.00057
Anastasia Terzi, S. Bibi, P. Sarigiannidis
JavaScript nowadays is among the most popular programming languages, used for developing web and IoT applications. Currently, the majority of JavaScript applications is reusing third-party components to acquire various functionalities. In this paper we isolate popular reused components and explore the type of functionality that is mostly being reused. Additionally, we examine whether the client applications adapt to the most recent versions of the reused components, and further study the reuse intensity of pairs of components that coexist in client applications. For this purpose, we performed a case study on 9389 components reused by 430 JavaScript applications hosted in GitHub. The results show that Compiler and Testing Frameworks are the most common types of functionality being reused, while the majority of client applications tend to adopt the recent versions of the reused components.
{"title":"Reuse Opportunities in JavaScript applications","authors":"Anastasia Terzi, S. Bibi, P. Sarigiannidis","doi":"10.1109/SEAA53835.2021.00057","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00057","url":null,"abstract":"JavaScript nowadays is among the most popular programming languages, used for developing web and IoT applications. Currently, the majority of JavaScript applications is reusing third-party components to acquire various functionalities. In this paper we isolate popular reused components and explore the type of functionality that is mostly being reused. Additionally, we examine whether the client applications adapt to the most recent versions of the reused components, and further study the reuse intensity of pairs of components that coexist in client applications. For this purpose, we performed a case study on 9389 components reused by 430 JavaScript applications hosted in GitHub. The results show that Compiler and Testing Frameworks are the most common types of functionality being reused, while the majority of client applications tend to adopt the recent versions of the reused components.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"65 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":"130841983","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/SEAA53835.2021.00019
Torvald Mårtensson, Daniel Ståhl, A. Martini, J. Bosch
Based on multiple series of interviews and workshops, this paper presents the MaLET model – a representation of the typical evolution path for companies who successfully adopt exploratory testing. The model provides a step-by-step approach to systematically improve exploratory testing over time, and shows how and why capabilities may regress. The MaLET model was validated through a series of interviews with 20 interviewees from eight case study companies in separate industry segments. The interviews also revealed examples on improvement initiatives that had failed in the companies, showing that improvement initiatives tend to fail if they are not planned in an order corresponding to the maturity levels in the model. The MaLET model was well received by the interviewees during the validation, describing the model as sound, relevant and useful in practice.
{"title":"The MaLET Model — Maturity Levels for Exploratory Testing","authors":"Torvald Mårtensson, Daniel Ståhl, A. Martini, J. Bosch","doi":"10.1109/SEAA53835.2021.00019","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00019","url":null,"abstract":"Based on multiple series of interviews and workshops, this paper presents the MaLET model – a representation of the typical evolution path for companies who successfully adopt exploratory testing. The model provides a step-by-step approach to systematically improve exploratory testing over time, and shows how and why capabilities may regress. The MaLET model was validated through a series of interviews with 20 interviewees from eight case study companies in separate industry segments. The interviews also revealed examples on improvement initiatives that had failed in the companies, showing that improvement initiatives tend to fail if they are not planned in an order corresponding to the maturity levels in the model. The MaLET model was well received by the interviewees during the validation, describing the model as sound, relevant and useful in practice.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"108 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":"124810314","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/SEAA53835.2021.00045
Joyce Aline Pereira de Oliveira, C. Alves
Software ecosystems play an important strategy in the IT industry. They involve the interaction of a set of actors on a common technological platform that results in a number of software solutions or services. Software ecosystems face intense competition and scrutiny of stakeholders and society as a whole. Therefore, they need to be aligned with principles of ethics, accountability and transparency. The governance of software ecosystems presents strategic procedures and processes to control, maintain, and evolve the platform. This article presents the application of a conceptual model for software ecosystem governance in the context of GNOME and SAP platforms. The case studies results suggest that governance of these platforms involve the need to manage software licenses, to define the type of collaboration in relation to governance (shared or closed), and to address social, technical and business risks in software ecosystems.
{"title":"Software Ecosystems Governance – An Analysis of SAP and GNOME Platforms","authors":"Joyce Aline Pereira de Oliveira, C. Alves","doi":"10.1109/SEAA53835.2021.00045","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00045","url":null,"abstract":"Software ecosystems play an important strategy in the IT industry. They involve the interaction of a set of actors on a common technological platform that results in a number of software solutions or services. Software ecosystems face intense competition and scrutiny of stakeholders and society as a whole. Therefore, they need to be aligned with principles of ethics, accountability and transparency. The governance of software ecosystems presents strategic procedures and processes to control, maintain, and evolve the platform. This article presents the application of a conceptual model for software ecosystem governance in the context of GNOME and SAP platforms. The case studies results suggest that governance of these platforms involve the need to manage software licenses, to define the type of collaboration in relation to governance (shared or closed), and to address social, technical and business risks in software ecosystems.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"69 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":"128624299","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/SEAA53835.2021.00018
Daniel Ståhl, Torvald Mårtensson
Continuous integration and delivery are well-established paradigms in the software development community. With these continuous practices come many challenges; while some of these challenges are immediate and well documented in literature, others may be revealed only after sustained application of these practices in large-scale and complex contexts. Based on researcher observations and interviews with 22 senior professionals from four companies, all with significant but varied experiences of continuous practices, we present a set of underreported challenges with continuous practices observed in multiple industry settings. Through a grounded theory approach we construct the Tapco model — Test Automation Progression in Continuous Practices — identifying two distinct ways in which companies progress towards continuous delivery, onto which the studied cases are mapped. This model is then validated by presenting it to the four studied companies, operating in disparate industry segments, and to three additional industry cases, letting them evaluate its relevancy, accuracy and novelty. We find that the model provides industry professionals with essential guidance on how to avoid common pitfalls, as well as an understanding of their causes and possible remediation.
{"title":"Won’t Somebody Please Think of the Tests? A Grounded Theory Approach to Industry Challenges in Continuous Practices","authors":"Daniel Ståhl, Torvald Mårtensson","doi":"10.1109/SEAA53835.2021.00018","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00018","url":null,"abstract":"Continuous integration and delivery are well-established paradigms in the software development community. With these continuous practices come many challenges; while some of these challenges are immediate and well documented in literature, others may be revealed only after sustained application of these practices in large-scale and complex contexts. Based on researcher observations and interviews with 22 senior professionals from four companies, all with significant but varied experiences of continuous practices, we present a set of underreported challenges with continuous practices observed in multiple industry settings. Through a grounded theory approach we construct the Tapco model — Test Automation Progression in Continuous Practices — identifying two distinct ways in which companies progress towards continuous delivery, onto which the studied cases are mapped. This model is then validated by presenting it to the four studied companies, operating in disparate industry segments, and to three additional industry cases, letting them evaluate its relevancy, accuracy and novelty. We find that the model provides industry professionals with essential guidance on how to avoid common pitfalls, as well as an understanding of their causes and possible remediation.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"298 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":"125417208","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/SEAA53835.2021.00056
L. Kumar, Prakhar Gupta, Lalita Bhanu Murthy Neti, S. K. Rath, Shashank Mouli Satapathy, Vipul Kocher, S. Padmanabhuni
Bug tracking is one of the prominent activities during the maintenance phase of software development. The severity of the bug acts as a key indicator of its criticality and impact towards planning evolution and maintenance of various types of software products. This indicator measures how negatively the bug may affect the system functionality. This helps in determining how quickly the development teams need to address the bug for successful execution of the software system. Due to a large number of bugs reported every day, the developers find it really difficult to assign the severity level to bugs accurately. Assigning incorrect severity level results in delaying the bug resolution process. Thus automated systems were developed which will assign a severity level using various machine learning techniques. In this work, five different types of sentence embedding techniques have been applied on bugs description to convert the description comments to an n-dimensional vector. These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. We have also considered different variants of the Synthetic Minority Oversampling Technique (SMOTE) to handle the class imbalance problem as the considered datasets are not evenly distributed. The experimental results on six projects highlight that the usage of sentence embedding, ensemble techniques, and different variants of SMOTE techniques helps in improving the predictive ability of defect severity level prediction models.
{"title":"Predicting Software Defect Severity Level using Sentence Embedding and Ensemble Learning","authors":"L. Kumar, Prakhar Gupta, Lalita Bhanu Murthy Neti, S. K. Rath, Shashank Mouli Satapathy, Vipul Kocher, S. Padmanabhuni","doi":"10.1109/SEAA53835.2021.00056","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00056","url":null,"abstract":"Bug tracking is one of the prominent activities during the maintenance phase of software development. The severity of the bug acts as a key indicator of its criticality and impact towards planning evolution and maintenance of various types of software products. This indicator measures how negatively the bug may affect the system functionality. This helps in determining how quickly the development teams need to address the bug for successful execution of the software system. Due to a large number of bugs reported every day, the developers find it really difficult to assign the severity level to bugs accurately. Assigning incorrect severity level results in delaying the bug resolution process. Thus automated systems were developed which will assign a severity level using various machine learning techniques. In this work, five different types of sentence embedding techniques have been applied on bugs description to convert the description comments to an n-dimensional vector. These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. We have also considered different variants of the Synthetic Minority Oversampling Technique (SMOTE) to handle the class imbalance problem as the considered datasets are not evenly distributed. The experimental results on six projects highlight that the usage of sentence embedding, ensemble techniques, and different variants of SMOTE techniques helps in improving the predictive ability of defect severity level prediction models.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"15 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":"131299685","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/SEAA53835.2021.00040
M. Jaakola, Tuisku Polvinen, Johannes Holvitie, Sherlock A. Licorish, Ville Leppänen
Short-term events, like hackathons, are commonly applied to innovate on novel subject areas and on ground-breaking technologies. While such events have led to significant successful outcomes for many companies, the big questions facing organisers of these events are: "where do we start?, what should be done towards the hosting of a successful event?, and what should be done when the event is completed?" While several methods and supporting recommendations have been introduced for answering these questions, a common framework for the hosting and management of short-term events is still not available. This is a limitation, as having such a framework would allow organisers to carry out systematic analysis for the subject area, identify the most suitable pre-event analysis and support functions, combine these with the fitting and justified short-term event types, and carry the results of the event to fruitful, sustainable businesses with post-event operations. The opportunity to progress events into business ventures is particularly noteworthy given the rapid changes that are typical in the technology domain, and thus, the need to be both innovative and nimble in responding to such changes and ensuing opportunities. To fill this gap, we introduce an evidence-driven framework for hosting short-term events. Our framework presents several chronological modules coupled together to form a comprehensive, but agile set of practices. A review of the state-of-the-art and a partial empirical trial support the framework’s utility, notwithstanding the need for further work to enhance and trial the framework for organising other events.
{"title":"From Setting Up Innovation in a Novel Context To Discovering Sustainable Business — A Framework for Short-Term Events","authors":"M. Jaakola, Tuisku Polvinen, Johannes Holvitie, Sherlock A. Licorish, Ville Leppänen","doi":"10.1109/SEAA53835.2021.00040","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00040","url":null,"abstract":"Short-term events, like hackathons, are commonly applied to innovate on novel subject areas and on ground-breaking technologies. While such events have led to significant successful outcomes for many companies, the big questions facing organisers of these events are: \"where do we start?, what should be done towards the hosting of a successful event?, and what should be done when the event is completed?\" While several methods and supporting recommendations have been introduced for answering these questions, a common framework for the hosting and management of short-term events is still not available. This is a limitation, as having such a framework would allow organisers to carry out systematic analysis for the subject area, identify the most suitable pre-event analysis and support functions, combine these with the fitting and justified short-term event types, and carry the results of the event to fruitful, sustainable businesses with post-event operations. The opportunity to progress events into business ventures is particularly noteworthy given the rapid changes that are typical in the technology domain, and thus, the need to be both innovative and nimble in responding to such changes and ensuing opportunities. To fill this gap, we introduce an evidence-driven framework for hosting short-term events. Our framework presents several chronological modules coupled together to form a comprehensive, but agile set of practices. A review of the state-of-the-art and a partial empirical trial support the framework’s utility, notwithstanding the need for further work to enhance and trial the framework for organising other events.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"24 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":"134519753","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/SEAA53835.2021.00035
Thomas F. Düllmann, Oliver Kabierschke, A. Hoorn
Today, most Continuous Integration and Delivery (CI/CD) solutions use infrastructure as code to describe the pipeline-based build and deployment process. Each solution uses its own format to describe the CI/CD pipeline, which hinders the interoperability and the analysis of CI/CD pipelines. In this paper, we propose a model-driven DSL-based framework for CI/CD pipeline definition and analysis. It comprises (i) the analysis of the meta-model of the Jenkins pipeline definition language, (ii) the StalkCD domain-specific language providing a base for interoperability and transformation between different formats, and (iii) an extensible set of transformations between tool-specific CI/CD definitions and analysis tools. We demonstrate the specific support for Jenkins as a CI/CD tool and BPMN for exploiting analyses from the workflow domain and visualizing the results. We evaluate the DSL and the transformations empirically based on more than 1,000 publicly available Jenkinsfiles. The evaluation shows that our framework supports 70% of these files without information loss.
{"title":"StalkCD: A Model-Driven Framework for Interoperability and Analysis of CI/CD Pipelines","authors":"Thomas F. Düllmann, Oliver Kabierschke, A. Hoorn","doi":"10.1109/SEAA53835.2021.00035","DOIUrl":"https://doi.org/10.1109/SEAA53835.2021.00035","url":null,"abstract":"Today, most Continuous Integration and Delivery (CI/CD) solutions use infrastructure as code to describe the pipeline-based build and deployment process. Each solution uses its own format to describe the CI/CD pipeline, which hinders the interoperability and the analysis of CI/CD pipelines. In this paper, we propose a model-driven DSL-based framework for CI/CD pipeline definition and analysis. It comprises (i) the analysis of the meta-model of the Jenkins pipeline definition language, (ii) the StalkCD domain-specific language providing a base for interoperability and transformation between different formats, and (iii) an extensible set of transformations between tool-specific CI/CD definitions and analysis tools. We demonstrate the specific support for Jenkins as a CI/CD tool and BPMN for exploiting analyses from the workflow domain and visualizing the results. We evaluate the DSL and the transformations empirically based on more than 1,000 publicly available Jenkinsfiles. The evaluation shows that our framework supports 70% of these files without information loss.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"48 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":"133338345","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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