際際滷shows by User: DavideRuscio / http://www.slideshare.net/images/logo.gif 際際滷shows by User: DavideRuscio / Tue, 27 Apr 2021 16:04:54 GMT 際際滷Share feed for 際際滷shows by User: DavideRuscio Developing recommendation systems to support open source software developers challenges and lessons learned /slideshow/developing-recommendation-systems-to-support-open-source-software-developers-challenges-and-lessons-learned/247140035 developingrecommendationsystemstosupportopensourcesoftwaredeveloperschallengesandlessonslearned-210427160454
Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open source developers with innovative features, aiming to obtain improvements in development e鍖ort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER project, different recommendation systems have been conceived to assist software programmers in di鍖erent phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture provides an introduction to Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER project. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed. https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021]]>
Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open source developers with innovative features, aiming to obtain improvements in development e鍖ort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER project, different recommendation systems have been conceived to assist software programmers in di鍖erent phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture provides an introduction to Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER project. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed. https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021]]> Tue, 27 Apr 2021 16:04:54 GMT /slideshow/developing-recommendation-systems-to-support-open-source-software-developers-challenges-and-lessons-learned/247140035 DavideRuscio@slideshare.net(DavideRuscio) Developing recommendation systems to support open source software developers challenges and lessons learned DavideRuscio Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open source developers with innovative features, aiming to obtain improvements in development e鍖ort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER project, different recommendation systems have been conceived to assist software programmers in di鍖erent phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture provides an introduction to Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER project. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed. https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/developingrecommendationsystemstosupportopensourcesoftwaredeveloperschallengesandlessonslearned-210427160454-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open source developers with innovative features, aiming to obtain improvements in development e鍖ort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER project, different recommendation systems have been conceived to assist software programmers in di鍖erent phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture provides an introduction to Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER project. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed. https://sites.google.com/gssi.it/csgssi/ph-d-program/se-ai-course-2021
Developing recommendation systems to support open source software developers challenges and lessons learned from Davide Ruscio
]]> 190 0 https://cdn.slidesharecdn.com/ss_thumbnails/developingrecommendationsystemstosupportopensourcesoftwaredeveloperschallengesandlessonslearned-210427160454-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Detecting java software similarities by using different clustering /DavideRuscio/detecting-java-software-similarities-by-using-different-clustering detectingjavasoftwaresimilaritiesbyusingdifferentclustering-201016155235
These are the slides of the talk I delivered at the Journal First session of ICSME 2020.]]>
These are the slides of the talk I delivered at the Journal First session of ICSME 2020.]]> Fri, 16 Oct 2020 15:52:35 GMT /DavideRuscio/detecting-java-software-similarities-by-using-different-clustering DavideRuscio@slideshare.net(DavideRuscio) Detecting java software similarities by using different clustering DavideRuscio These are the slides of the talk I delivered at the Journal First session of ICSME 2020. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/detectingjavasoftwaresimilaritiesbyusingdifferentclustering-201016155235-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> These are the slides of the talk I delivered at the Journal First session of ICSME 2020.
Detecting java software similarities by using different clustering from Davide Ruscio
]]> 118 0 https://cdn.slidesharecdn.com/ss_thumbnails/detectingjavasoftwaresimilaritiesbyusingdifferentclustering-201016155235-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 On the way of listening to the crowd for supporting modeling activities /slideshow/on-the-way-of-listening-to-the-crowd-for-supporting-modeling-activities/238893810 onthewayoflisteningtothecrowdforsupportingmodelingactivities-201016120943
These are the slides of the talk I delivered at the AMMORE+ME workshop at MODELS2020. http://www.models-and-evolution.com/2020/ ]]>
These are the slides of the talk I delivered at the AMMORE+ME workshop at MODELS2020. http://www.models-and-evolution.com/2020/ ]]> Fri, 16 Oct 2020 12:09:43 GMT /slideshow/on-the-way-of-listening-to-the-crowd-for-supporting-modeling-activities/238893810 DavideRuscio@slideshare.net(DavideRuscio) On the way of listening to the crowd for supporting modeling activities DavideRuscio These are the slides of the talk I delivered at the AMMORE+ME workshop at MODELS2020. http://www.models-and-evolution.com/2020/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/onthewayoflisteningtothecrowdforsupportingmodelingactivities-201016120943-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> These are the slides of the talk I delivered at the AMMORE+ME workshop at MODELS2020. http://www.models-and-evolution.com/2020/
On the way of listening to the crowd for supporting modeling activities from Davide Ruscio
]]> 175 0 https://cdn.slidesharecdn.com/ss_thumbnails/onthewayoflisteningtothecrowdforsupportingmodelingactivities-201016120943-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns /slideshow/focus-a-recommender-system-for-mining-api-function-calls-and-usage-patterns/148485464 icse19focus-3-190531202508
Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.]]>
Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.]]> Fri, 31 May 2019 20:25:08 GMT /slideshow/focus-a-recommender-system-for-mining-api-function-calls-and-usage-patterns/148485464 DavideRuscio@slideshare.net(DavideRuscio) FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns DavideRuscio Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icse19focus-3-190531202508-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.
FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns from Davide Ruscio
]]> 380 1 https://cdn.slidesharecdn.com/ss_thumbnails/icse19focus-3-190531202508-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 CrossSim: exploiting mutual relationships to detect similar OSS projects /slideshow/crosssim-exploiting-mutual-relationships-to-detect-similar-oss-projects/112387013 seaa18crosssim-180831080729
際際滷s presented at SEAA 2018 http://dsd-seaa2018.fit.cvut.cz/seaa/ related to the paper http://reposto.di.univaq.it/aigon2/index.php/attachments/single/211 Software development is a knowledge-intensive activity, which requires mastering several languages, frameworks, technology trends (among other aspects) under the pressure of ever-increasing arrays of external libraries and resources. Recommender systems are gaining high relevance in software engineering since they aim at providing developers with real-time recommendations, which can reduce the time spent on discovering and understanding reusable artifacts from software repositories, and thus inducing productivity and quality gains. In this presentation, we focus on the problem of mining open source software repositories to identify similar projects, which can be evaluated and eventually reused by developers. To this end, CROSSSIM is proposed as a novel approach to model open source software projects and related artifacts and to compute similarities among them. An evaluation on a dataset containing 580 GitHub projects shows that CROSSSIM outperforms an existing technique, which has been proven to have a good performance in detecting similar GitHub repositories. ]]>
際際滷s presented at SEAA 2018 http://dsd-seaa2018.fit.cvut.cz/seaa/ related to the paper http://reposto.di.univaq.it/aigon2/index.php/attachments/single/211 Software development is a knowledge-intensive activity, which requires mastering several languages, frameworks, technology trends (among other aspects) under the pressure of ever-increasing arrays of external libraries and resources. Recommender systems are gaining high relevance in software engineering since they aim at providing developers with real-time recommendations, which can reduce the time spent on discovering and understanding reusable artifacts from software repositories, and thus inducing productivity and quality gains. In this presentation, we focus on the problem of mining open source software repositories to identify similar projects, which can be evaluated and eventually reused by developers. To this end, CROSSSIM is proposed as a novel approach to model open source software projects and related artifacts and to compute similarities among them. An evaluation on a dataset containing 580 GitHub projects shows that CROSSSIM outperforms an existing technique, which has been proven to have a good performance in detecting similar GitHub repositories. ]]> Fri, 31 Aug 2018 08:07:29 GMT /slideshow/crosssim-exploiting-mutual-relationships-to-detect-similar-oss-projects/112387013 DavideRuscio@slideshare.net(DavideRuscio) CrossSim: exploiting mutual relationships to detect similar OSS projects DavideRuscio 際際滷s presented at SEAA 2018 http://dsd-seaa2018.fit.cvut.cz/seaa/ related to the paper http://reposto.di.univaq.it/aigon2/index.php/attachments/single/211 Software development is a knowledge-intensive activity, which requires mastering several languages, frameworks, technology trends (among other aspects) under the pressure of ever-increasing arrays of external libraries and resources. Recommender systems are gaining high relevance in software engineering since they aim at providing developers with real-time recommendations, which can reduce the time spent on discovering and understanding reusable artifacts from software repositories, and thus inducing productivity and quality gains. In this presentation, we focus on the problem of mining open source software repositories to identify similar projects, which can be evaluated and eventually reused by developers. To this end, CROSSSIM is proposed as a novel approach to model open source software projects and related artifacts and to compute similarities among them. An evaluation on a dataset containing 580 GitHub projects shows that CROSSSIM outperforms an existing technique, which has been proven to have a good performance in detecting similar GitHub repositories. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/seaa18crosssim-180831080729-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s presented at SEAA 2018 http://dsd-seaa2018.fit.cvut.cz/seaa/ related to the paper http://reposto.di.univaq.it/aigon2/index.php/attachments/single/211 Software development is a knowledge-intensive activity, which requires mastering several languages, frameworks, technology trends (among other aspects) under the pressure of ever-increasing arrays of external libraries and resources. Recommender systems are gaining high relevance in software engineering since they aim at providing developers with real-time recommendations, which can reduce the time spent on discovering and understanding reusable artifacts from software repositories, and thus inducing productivity and quality gains. In this presentation, we focus on the problem of mining open source software repositories to identify similar projects, which can be evaluated and eventually reused by developers. To this end, CROSSSIM is proposed as a novel approach to model open source software projects and related artifacts and to compute similarities among them. An evaluation on a dataset containing 580 GitHub projects shows that CROSSSIM outperforms an existing technique, which has been proven to have a good performance in detecting similar GitHub repositories.
CrossSim: exploiting mutual relationships to detect similar OSS projects from Davide Ruscio
]]> 437 2 https://cdn.slidesharecdn.com/ss_thumbnails/seaa18crosssim-180831080729-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Use of MDE to Analyse Open Source Software /slideshow/use-of-mde-to-analyse-open-source-software/86576663 moma3ninvitedtalk-180123111434
Use of MDE to Analyse Open Source Software]]>
Use of MDE to Analyse Open Source Software]]> Tue, 23 Jan 2018 11:14:34 GMT /slideshow/use-of-mde-to-analyse-open-source-software/86576663 DavideRuscio@slideshare.net(DavideRuscio) Use of MDE to Analyse Open Source Software DavideRuscio Use of MDE to Analyse Open Source Software <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/moma3ninvitedtalk-180123111434-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Use of MDE to Analyse Open Source Software
Use of MDE to Analyse Open Source Software from Davide Ruscio
]]> 583 4 https://cdn.slidesharecdn.com/ss_thumbnails/moma3ninvitedtalk-180123111434-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Consistency Recovery in Interactive Modeling /slideshow/consistency-recovery-in-interactive-modeling/79908895 exe-at-models2017-170918203814
MDE projects contain different kinds of artifacts such as models, metamodels, model transformations, and deltas. These artifacts are related in terms of relationships such as transformation or conformance. In this presentation, we capture the types of artifacts and the relevant relationships in a megamodeling-based manner for the purpose of monitoring and recovering project consistency in response to changes that users may apply to the project within an interactive modeling platform. The approach supports users in experimenting with MDE projects and receiving feedback upon changes on the grounds of a specific execution semantics for megamodels. The approach is validated within the web-based modeling platform MDEFORGE.]]>
MDE projects contain different kinds of artifacts such as models, metamodels, model transformations, and deltas. These artifacts are related in terms of relationships such as transformation or conformance. In this presentation, we capture the types of artifacts and the relevant relationships in a megamodeling-based manner for the purpose of monitoring and recovering project consistency in response to changes that users may apply to the project within an interactive modeling platform. The approach supports users in experimenting with MDE projects and receiving feedback upon changes on the grounds of a specific execution semantics for megamodels. The approach is validated within the web-based modeling platform MDEFORGE.]]> Mon, 18 Sep 2017 20:38:14 GMT /slideshow/consistency-recovery-in-interactive-modeling/79908895 DavideRuscio@slideshare.net(DavideRuscio) Consistency Recovery in Interactive Modeling DavideRuscio MDE projects contain different kinds of artifacts such as models, metamodels, model transformations, and deltas. These artifacts are related in terms of relationships such as transformation or conformance. In this presentation, we capture the types of artifacts and the relevant relationships in a megamodeling-based manner for the purpose of monitoring and recovering project consistency in response to changes that users may apply to the project within an interactive modeling platform. The approach supports users in experimenting with MDE projects and receiving feedback upon changes on the grounds of a specific execution semantics for megamodels. The approach is validated within the web-based modeling platform MDEFORGE. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/exe-at-models2017-170918203814-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MDE projects contain different kinds of artifacts such as models, metamodels, model transformations, and deltas. These artifacts are related in terms of relationships such as transformation or conformance. In this presentation, we capture the types of artifacts and the relevant relationships in a megamodeling-based manner for the purpose of monitoring and recovering project consistency in response to changes that users may apply to the project within an interactive modeling platform. The approach supports users in experimenting with MDE projects and receiving feedback upon changes on the grounds of a specific execution semantics for megamodels. The approach is validated within the web-based modeling platform MDEFORGE.
Consistency Recovery in Interactive Modeling from Davide Ruscio
]]> 326 5 https://cdn.slidesharecdn.com/ss_thumbnails/exe-at-models2017-170918203814-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Edelta: an approach for defining and applying reusable metamodel refactorings /slideshow/edelta-an-approach-for-defining-and-applying-reusable-metamodel-refactorings/79901128 me2017-170918164205
Metamodels can be considered one of the key artifacts of any model-based project. Similarly to other software artifacts, metamodels are expected to evolve during their life-cycle and consequently it is crucial to develop approaches and tools supporting the definition and re-use of metamodel refactorings in a disciplined way. This paper proposes Edelta, a domain specific language for specifying reusable libraries of metamodel refactorings. The language allows both atomic and complex changes and it is supported by an Eclipse-based IDE. The developed supporting environment allows the developer to apply refactorings both in a batch manner and in a step-by-step fashion, which provides developers with an immediate view of the evolving Ecore model before actually changing it.]]>
Metamodels can be considered one of the key artifacts of any model-based project. Similarly to other software artifacts, metamodels are expected to evolve during their life-cycle and consequently it is crucial to develop approaches and tools supporting the definition and re-use of metamodel refactorings in a disciplined way. This paper proposes Edelta, a domain specific language for specifying reusable libraries of metamodel refactorings. The language allows both atomic and complex changes and it is supported by an Eclipse-based IDE. The developed supporting environment allows the developer to apply refactorings both in a batch manner and in a step-by-step fashion, which provides developers with an immediate view of the evolving Ecore model before actually changing it.]]> Mon, 18 Sep 2017 16:42:05 GMT /slideshow/edelta-an-approach-for-defining-and-applying-reusable-metamodel-refactorings/79901128 DavideRuscio@slideshare.net(DavideRuscio) Edelta: an approach for defining and applying reusable metamodel refactorings DavideRuscio Metamodels can be considered one of the key artifacts of any model-based project. Similarly to other software artifacts, metamodels are expected to evolve during their life-cycle and consequently it is crucial to develop approaches and tools supporting the definition and re-use of metamodel refactorings in a disciplined way. This paper proposes Edelta, a domain specific language for specifying reusable libraries of metamodel refactorings. The language allows both atomic and complex changes and it is supported by an Eclipse-based IDE. The developed supporting environment allows the developer to apply refactorings both in a batch manner and in a step-by-step fashion, which provides developers with an immediate view of the evolving Ecore model before actually changing it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/me2017-170918164205-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Metamodels can be considered one of the key artifacts of any model-based project. Similarly to other software artifacts, metamodels are expected to evolve during their life-cycle and consequently it is crucial to develop approaches and tools supporting the definition and re-use of metamodel refactorings in a disciplined way. This paper proposes Edelta, a domain specific language for specifying reusable libraries of metamodel refactorings. The language allows both atomic and complex changes and it is supported by an Eclipse-based IDE. The developed supporting environment allows the developer to apply refactorings both in a batch manner and in a step-by-step fashion, which provides developers with an immediate view of the evolving Ecore model before actually changing it.
Edelta: an approach for defining and applying reusable metamodel refactorings from Davide Ruscio
]]> 374 2 https://cdn.slidesharecdn.com/ss_thumbnails/me2017-170918164205-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Semantic based model matching with emf compare /slideshow/semantic-based-model-matching-with-emf-compare/66709844 semantic-basedmodelmatchingwithemfcompare-161004095414
In MDE resolving pragmatic issues related to the management of models is key to success. Model comparison is one of the most challenging operations playing a central role in a wide range of modelling activities including model versioning, evolution and even collaborative and distributed specification of models. Over the last decade, several syntactic methods have been proposed to compare models even though they struggle in achieving higher levels of accuracy especially when the semantics of the application domain has to be considered. Existing methods improve comparison precision at the price of high performance costs. In this talk I presented a lightweight semantic comparison method, which relies on a new matching algorithm that considers ontological information encoded in the WordNet lexical database further than ordinary syntactical and structural correlations. The approach has been implemented as extension of EMFCompare and evaluated to measure its precision and performances when compared to existing approaches.]]>
In MDE resolving pragmatic issues related to the management of models is key to success. Model comparison is one of the most challenging operations playing a central role in a wide range of modelling activities including model versioning, evolution and even collaborative and distributed specification of models. Over the last decade, several syntactic methods have been proposed to compare models even though they struggle in achieving higher levels of accuracy especially when the semantics of the application domain has to be considered. Existing methods improve comparison precision at the price of high performance costs. In this talk I presented a lightweight semantic comparison method, which relies on a new matching algorithm that considers ontological information encoded in the WordNet lexical database further than ordinary syntactical and structural correlations. The approach has been implemented as extension of EMFCompare and evaluated to measure its precision and performances when compared to existing approaches.]]> Tue, 04 Oct 2016 09:54:14 GMT /slideshow/semantic-based-model-matching-with-emf-compare/66709844 DavideRuscio@slideshare.net(DavideRuscio) Semantic based model matching with emf compare DavideRuscio In MDE resolving pragmatic issues related to the management of models is key to success. Model comparison is one of the most challenging operations playing a central role in a wide range of modelling activities including model versioning, evolution and even collaborative and distributed specification of models. Over the last decade, several syntactic methods have been proposed to compare models even though they struggle in achieving higher levels of accuracy especially when the semantics of the application domain has to be considered. Existing methods improve comparison precision at the price of high performance costs. In this talk I presented a lightweight semantic comparison method, which relies on a new matching algorithm that considers ontological information encoded in the WordNet lexical database further than ordinary syntactical and structural correlations. The approach has been implemented as extension of EMFCompare and evaluated to measure its precision and performances when compared to existing approaches. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/semantic-basedmodelmatchingwithemfcompare-161004095414-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In MDE resolving pragmatic issues related to the management of models is key to success. Model comparison is one of the most challenging operations playing a central role in a wide range of modelling activities including model versioning, evolution and even collaborative and distributed specification of models. Over the last decade, several syntactic methods have been proposed to compare models even though they struggle in achieving higher levels of accuracy especially when the semantics of the application domain has to be considered. Existing methods improve comparison precision at the price of high performance costs. In this talk I presented a lightweight semantic comparison method, which relies on a new matching algorithm that considers ontological information encoded in the WordNet lexical database further than ordinary syntactical and structural correlations. The approach has been implemented as extension of EMFCompare and evaluated to measure its precision and performances when compared to existing approaches.
Semantic based model matching with emf compare from Davide Ruscio
]]> 371 2 https://cdn.slidesharecdn.com/ss_thumbnails/semantic-basedmodelmatchingwithemfcompare-161004095414-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Collaborative model driven software engineering: a Systematic Mapping Study /slideshow/collaborative-model-driven-software-engineering-a-systematic-mapping-study/66709470 collaborativemodel-drivensoftwareengineering-161004094521
Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations. A collaborative MDSE approach can be defined as a method or technique allowing multiple stakeholders to work on a set of shared modeling artifacts, and to be aware of each others work. Even though Collaborative MDSE is gaining a growing interest in both academia and practice, a holistic view on what Collaborative MDSE is, its components, the related opportunities and challenges is still missing. In this talk, I outlined the main insights of the systematic mapping study we have done to identify and classify approaches, methods, and techniques that support collaborative. We present three complementary dimensions that we have identified during the study as the peculiar aspects building up a collaborative MDSE: a model management infrastructure for managing the life cycle of the models, a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and a set of communication means for allowing involved stakeholders to be aware of the activities of the other stakeholders. The identification of limitations and challenges of currently available collaborative MDE approaches is also given by discussing the implications for future investigation. ]]>
Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations. A collaborative MDSE approach can be defined as a method or technique allowing multiple stakeholders to work on a set of shared modeling artifacts, and to be aware of each others work. Even though Collaborative MDSE is gaining a growing interest in both academia and practice, a holistic view on what Collaborative MDSE is, its components, the related opportunities and challenges is still missing. In this talk, I outlined the main insights of the systematic mapping study we have done to identify and classify approaches, methods, and techniques that support collaborative. We present three complementary dimensions that we have identified during the study as the peculiar aspects building up a collaborative MDSE: a model management infrastructure for managing the life cycle of the models, a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and a set of communication means for allowing involved stakeholders to be aware of the activities of the other stakeholders. The identification of limitations and challenges of currently available collaborative MDE approaches is also given by discussing the implications for future investigation. ]]> Tue, 04 Oct 2016 09:45:20 GMT /slideshow/collaborative-model-driven-software-engineering-a-systematic-mapping-study/66709470 DavideRuscio@slideshare.net(DavideRuscio) Collaborative model driven software engineering: a Systematic Mapping Study DavideRuscio Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations. A collaborative MDSE approach can be defined as a method or technique allowing multiple stakeholders to work on a set of shared modeling artifacts, and to be aware of each others work. Even though Collaborative MDSE is gaining a growing interest in both academia and practice, a holistic view on what Collaborative MDSE is, its components, the related opportunities and challenges is still missing. In this talk, I outlined the main insights of the systematic mapping study we have done to identify and classify approaches, methods, and techniques that support collaborative. We present three complementary dimensions that we have identified during the study as the peculiar aspects building up a collaborative MDSE: a model management infrastructure for managing the life cycle of the models, a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and a set of communication means for allowing involved stakeholders to be aware of the activities of the other stakeholders. The identification of limitations and challenges of currently available collaborative MDE approaches is also given by discussing the implications for future investigation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/collaborativemodel-drivensoftwareengineering-161004094521-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Collaborative software engineering (CoSE) deals with methods, processes and tools for enhancing collaboration, communication, and co-ordination (3C) among team members. CoSE can be employed to conceive different kinds of artifacts during the development and evolution of software systems. For instance, when focusing on software design, multiple stakeholders with different expertise and responsibility collaborate on the system design. Model-Driven Software Engineering (MDSE) provides suitable techniques and tools for specifying, manipulating, and analyzing modeling artifacts including metamodels, models, and transformations. A collaborative MDSE approach can be defined as a method or technique allowing multiple stakeholders to work on a set of shared modeling artifacts, and to be aware of each others work. Even though Collaborative MDSE is gaining a growing interest in both academia and practice, a holistic view on what Collaborative MDSE is, its components, the related opportunities and challenges is still missing. In this talk, I outlined the main insights of the systematic mapping study we have done to identify and classify approaches, methods, and techniques that support collaborative. We present three complementary dimensions that we have identified during the study as the peculiar aspects building up a collaborative MDSE: a model management infrastructure for managing the life cycle of the models, a set of collaboration means for allowing involved stakeholders to work on the modelling artifacts collaboratively, and a set of communication means for allowing involved stakeholders to be aware of the activities of the other stakeholders. The identification of limitations and challenges of currently available collaborative MDE approaches is also given by discussing the implications for future investigation.
Collaborative model driven software engineering: a Systematic Mapping Study from Davide Ruscio
]]> 746 8 https://cdn.slidesharecdn.com/ss_thumbnails/collaborativemodel-drivensoftwareengineering-161004094521-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Model repositories: will they become reality? /slideshow/model-repositories-will-they-become-reality/53395436 modelrepositories-150930221040-lva1-app6891
際際滷s of my talk at CloudMDE workshop @ MODELS 2015 - Ottawa, Canada]]>
際際滷s of my talk at CloudMDE workshop @ MODELS 2015 - Ottawa, Canada]]> Wed, 30 Sep 2015 22:10:39 GMT /slideshow/model-repositories-will-they-become-reality/53395436 DavideRuscio@slideshare.net(DavideRuscio) Model repositories: will they become reality? DavideRuscio 際際滷s of my talk at CloudMDE workshop @ MODELS 2015 - Ottawa, Canada <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modelrepositories-150930221040-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s of my talk at CloudMDE workshop @ MODELS 2015 - Ottawa, Canada
Model repositories: will they become reality? from Davide Ruscio
]]> 622 8 https://cdn.slidesharecdn.com/ss_thumbnails/modelrepositories-150930221040-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mining Correlations of 鐃ATL Transformation and Metamodel Metrics鐃 /slideshow/mining-correlations-of-atl-transformation-and-metamodel-metrics/48659498 mise15-150527140235-lva1-app6892
Model transformations are considered to be the heart and soul of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this talk, a process to analyze model transformations is discussed with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The talk discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels. The slides have been used to present the paper "Mining Correlations of ATL Transformation and Metamodel Metrics" at MISE2015 workshop at ICSE2015 (http://goo.gl/UJ9nWC)]]>
Model transformations are considered to be the heart and soul of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this talk, a process to analyze model transformations is discussed with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The talk discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels. The slides have been used to present the paper "Mining Correlations of ATL Transformation and Metamodel Metrics" at MISE2015 workshop at ICSE2015 (http://goo.gl/UJ9nWC)]]> Wed, 27 May 2015 14:02:35 GMT /slideshow/mining-correlations-of-atl-transformation-and-metamodel-metrics/48659498 DavideRuscio@slideshare.net(DavideRuscio) Mining Correlations of 鐃ATL Transformation and Metamodel Metrics鐃 DavideRuscio Model transformations are considered to be the heart and soul of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this talk, a process to analyze model transformations is discussed with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The talk discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels. The slides have been used to present the paper "Mining Correlations of 鐃ATL Transformation and Metamodel Metrics鐃" at MISE2015 workshop at ICSE2015 (http://goo.gl/UJ9nWC) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mise15-150527140235-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Model transformations are considered to be the heart and soul of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this talk, a process to analyze model transformations is discussed with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The talk discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels. The slides have been used to present the paper &quot;Mining Correlations of 鐃ATL Transformation and Metamodel Metrics鐃&quot; at MISE2015 workshop at ICSE2015 (http://goo.gl/UJ9nWC)
Mining Correlations of ATL Transformation and Metamodel Metrics from Davide Ruscio
]]> 314 2 https://cdn.slidesharecdn.com/ss_thumbnails/mise15-150527140235-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 MDEForge: an extensible Web-based modeling platform /slideshow/mde-forge/39743053 mdeforge-141001044321-phpapp01
Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this presentation we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts.~Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures.]]>
Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this presentation we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts.~Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures.]]> Wed, 01 Oct 2014 04:43:20 GMT /slideshow/mde-forge/39743053 DavideRuscio@slideshare.net(DavideRuscio) MDEForge: an extensible Web-based modeling platform DavideRuscio Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this presentation we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts.~Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mdeforge-141001044321-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this presentation we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts.~Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures.
MDEForge: an extensible Web-based modeling platform from Davide Ruscio
]]> 715 2 https://cdn.slidesharecdn.com/ss_thumbnails/mdeforge-141001044321-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-DavideRuscio-48x48.jpg?cb=1728995452 Davide Di Ruscio is Associate Professor at the Department of Information Engineering Computer Science and Mathematics of the University of LAquila. His main research interests are related to several aspects of Software Engineering, Open Source Software, and Model Driven Engineering (MDE) including domain specific modelling languages, model transformation, model differencing, model evolution, and coupled evolution. people.disim.univaq.it/diruscio/ https://cdn.slidesharecdn.com/ss_thumbnails/developingrecommendationsystemstosupportopensourcesoftwaredeveloperschallengesandlessonslearned-210427160454-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/developing-recommendation-systems-to-support-open-source-software-developers-challenges-and-lessons-learned/247140035 Developing recommendat... https://cdn.slidesharecdn.com/ss_thumbnails/detectingjavasoftwaresimilaritiesbyusingdifferentclustering-201016155235-thumbnail.jpg?width=320&height=320&fit=bounds DavideRuscio/detecting-java-software-similarities-by-using-different-clustering Detecting java softwar... https://cdn.slidesharecdn.com/ss_thumbnails/onthewayoflisteningtothecrowdforsupportingmodelingactivities-201016120943-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/on-the-way-of-listening-to-the-crowd-for-supporting-modeling-activities/238893810 On the way of listenin...