Pub Date : 2017-05-20DOI: 10.1109/SE4Science.2017.10
Suraj Kamble, Xiaoyu Jin, Nan Niu, Michelle Simon
Computational science and engineering (CSE) software is written by experts of certain area(s). Due to the specialization, existing CSE software may need to integrate other CSE software systems developed by different groups of experts. The coupling problem is one of the challenges for software integration. Here, the coupling we study means the issues involved in integrating 2 legacy codes together, but not the multiphysics coupling where distinct codes are combined in order to model particular phenomena. In this paper, we identify a complex coupling pattern when trying to integrate two CSE software systems. We describe the coupling pattern in detail and show the complexity of resolving such kind of coupling patterns. Our work contributes to area of CSE software since there were few previous studies addressing the coupling problem in CSE domain. Our work will further inspire future research in solving the coupling problem during CSE software integration.
{"title":"A Novel Coupling Pattern in Computational Science and Engineering Software","authors":"Suraj Kamble, Xiaoyu Jin, Nan Niu, Michelle Simon","doi":"10.1109/SE4Science.2017.10","DOIUrl":"https://doi.org/10.1109/SE4Science.2017.10","url":null,"abstract":"Computational science and engineering (CSE) software is written by experts of certain area(s). Due to the specialization, existing CSE software may need to integrate other CSE software systems developed by different groups of experts. The coupling problem is one of the challenges for software integration. Here, the coupling we study means the issues involved in integrating 2 legacy codes together, but not the multiphysics coupling where distinct codes are combined in order to model particular phenomena. In this paper, we identify a complex coupling pattern when trying to integrate two CSE software systems. We describe the coupling pattern in detail and show the complexity of resolving such kind of coupling patterns. Our work contributes to area of CSE software since there were few previous studies addressing the coupling problem in CSE domain. Our work will further inspire future research in solving the coupling problem during CSE software integration.","PeriodicalId":318588,"journal":{"name":"2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241897","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 : 2017-05-20DOI: 10.1109/SE4Science.2017.11
Jonathan Bell, Thomas D. Latoza, Foteini Baldimtsi, A. Stavrou
The scientific community is facing a crisis of reproducibility: confidence in scientific results is damaged by concerns regarding the integrity of experimental data and the analyses applied to that data. Experimental integrity can be compromised inadvertently when researchers overlook some important component of their experimental procedure, or intentionally by researchers or malicious third-parties who are biased towards ensuring a specific outcome of an experiment. The scientific community has pushed for "open science" to add transparency to the experimental process, asking researchers to publicly register their data sets and experimental procedures. We argue that the software engineering community can leverage its expertise in tracking traceability and provenance of source code and its related artifacts to simplify data management for scientists. Moreover, by leveraging smart contract and blockchain technologies, we believe that it is possible for such a system to guarantee end-to-end integrity of scientific data and results while supporting collaborative research.
{"title":"Advancing Open Science with Version Control and Blockchains","authors":"Jonathan Bell, Thomas D. Latoza, Foteini Baldimtsi, A. Stavrou","doi":"10.1109/SE4Science.2017.11","DOIUrl":"https://doi.org/10.1109/SE4Science.2017.11","url":null,"abstract":"The scientific community is facing a crisis of reproducibility: confidence in scientific results is damaged by concerns regarding the integrity of experimental data and the analyses applied to that data. Experimental integrity can be compromised inadvertently when researchers overlook some important component of their experimental procedure, or intentionally by researchers or malicious third-parties who are biased towards ensuring a specific outcome of an experiment. The scientific community has pushed for \"open science\" to add transparency to the experimental process, asking researchers to publicly register their data sets and experimental procedures. We argue that the software engineering community can leverage its expertise in tracking traceability and provenance of source code and its related artifacts to simplify data management for scientists. Moreover, by leveraging smart contract and blockchain technologies, we believe that it is possible for such a system to guarantee end-to-end integrity of scientific data and results while supporting collaborative research.","PeriodicalId":318588,"journal":{"name":"2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129682460","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 : 2017-05-01DOI: 10.1109/SE4Science.2017.13
Anawat Leatongkam, Aziz Nanthaamornphong, D. Rouson
Fortran finds widespread use in scientific and engineering communities that embraced computing early, including weather and climate science and mechanical, nuclear, and aerospace engineering. Over its lifetime, Fortran has evolved to support multiple programming paradigms, including Object-Oriented Programming (OOP). Despite the recently burgeoning ecosystem of tools and libraries supporting modern Fortran, there remains limited support for generating common Object-Oriented Design (OOD) diagrams from Fortran source code. ForUML partially fills this need by reverse engineering Unified Modeling Language (UML) class diagrams from object-oriented (OO) Fortran programs. Class diagrams provide useful information about class structures and inter-relationships, but class diagrams do not convey the temporal information required to understand runtime class behavior and interactions. UML sequence diagrams provide such important algorithmic details. This paper proposes to extend ForUML to extract UML sequence diagrams from Fortran code and to offer this capability via a widely used open-source platform. The paper argues that the proposed capability can raise the level of abstraction at which the computational science community discusses modern Fortran.
{"title":"WIP: Generating Sequence Diagrams for Modern Fortran","authors":"Anawat Leatongkam, Aziz Nanthaamornphong, D. Rouson","doi":"10.1109/SE4Science.2017.13","DOIUrl":"https://doi.org/10.1109/SE4Science.2017.13","url":null,"abstract":"Fortran finds widespread use in scientific and engineering communities that embraced computing early, including weather and climate science and mechanical, nuclear, and aerospace engineering. Over its lifetime, Fortran has evolved to support multiple programming paradigms, including Object-Oriented Programming (OOP). Despite the recently burgeoning ecosystem of tools and libraries supporting modern Fortran, there remains limited support for generating common Object-Oriented Design (OOD) diagrams from Fortran source code. ForUML partially fills this need by reverse engineering Unified Modeling Language (UML) class diagrams from object-oriented (OO) Fortran programs. Class diagrams provide useful information about class structures and inter-relationships, but class diagrams do not convey the temporal information required to understand runtime class behavior and interactions. UML sequence diagrams provide such important algorithmic details. This paper proposes to extend ForUML to extract UML sequence diagrams from Fortran code and to offer this capability via a widely used open-source platform. The paper argues that the proposed capability can raise the level of abstraction at which the computational science community discusses modern Fortran.","PeriodicalId":318588,"journal":{"name":"2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)","volume":"695 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125289941","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 : 2017-05-01DOI: 10.1109/SE4Science.2017.14
W. Macey, Dali Wang, P. Thornton, A. Mockus
In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe data architecture of a land component within theACME project while the project is undergoing activedevelopment. The data architect for the land subsystemdefines the new datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primarydata-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.
{"title":"WIP: Live Restructuring of Data Architecture","authors":"W. Macey, Dali Wang, P. Thornton, A. Mockus","doi":"10.1109/SE4Science.2017.14","DOIUrl":"https://doi.org/10.1109/SE4Science.2017.14","url":null,"abstract":"In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe data architecture of a land component within theACME project while the project is undergoing activedevelopment. The data architect for the land subsystemdefines the new datatype requirements that wouldgreatly simplify the implementation of terrestrial landsubmodels by converting more than 50 to just eight primarydata-types. Since the code is developed with thecommunity governance, we have to ensure that the restructuringdoes not interface the other developmentwhich, with dozens of changes occurring every day, makeit impossible to work on a shared development branch. The active development also occurs on almost five hundredbranches, making it extremely difficult to assesspotential interactions. To address these challenges we have designed andstarted an iterative procedure for implementing the datarestructuring and estimating both the effort it takes torestructure and the effort would save once the restructuringis implemented.","PeriodicalId":318588,"journal":{"name":"2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117221385","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 : 1900-01-01DOI: 10.1109/SE4Science.2017.12
Lenita M. Ambrósio, J. M. David, Regina M. M. Braga, Victor Ströele A. Menezes, Fernanda Campos, M. A. Araújo
The management of provenance information plays a key role in the scientific experimentation domain, since scientists often need to examine and audit the results obtained from experiments. In addition, provenance data are essential to ensure reproducibility and reuse of experiments or artifacts produced by them. In this way, the objective of this work is to present an ontology to support the researchers in the process of scientific experimentation using provenance data. These data assist in the reproduction and reuse of scientific experiments, as well as allow the discovery of implicit knowledge using inference mechanisms.
{"title":"WIP: Prov-SE-O: A Provenance Ontology to Support Scientists in Scientific Experimentation Process","authors":"Lenita M. Ambrósio, J. M. David, Regina M. M. Braga, Victor Ströele A. Menezes, Fernanda Campos, M. A. Araújo","doi":"10.1109/SE4Science.2017.12","DOIUrl":"https://doi.org/10.1109/SE4Science.2017.12","url":null,"abstract":"The management of provenance information plays a key role in the scientific experimentation domain, since scientists often need to examine and audit the results obtained from experiments. In addition, provenance data are essential to ensure reproducibility and reuse of experiments or artifacts produced by them. In this way, the objective of this work is to present an ontology to support the researchers in the process of scientific experimentation using provenance data. These data assist in the reproduction and reuse of scientific experiments, as well as allow the discovery of implicit knowledge using inference mechanisms.","PeriodicalId":318588,"journal":{"name":"2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797802","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：