Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and so...Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.展开更多
Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models...Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models and best practices, we propose a methodology and an integrated environment for business process modeling driven by the metamodel. Furthermore, we propose a process-template design method to unify the granularity and separate the commonality and variability of business processes so that business process models can be reused across different enterprise software systems. The proposed methodology enables to create reuse-oriented business process templates before the business process modeling. To support the proposed methodology, we developed an integrated environment for creating, reusing and verifying the business process models. As the key techniques, we describe the methodology and its integrated environment, including a metamodel and notations. We applied the methodology and integrated environment to an actual enterprise software development project, and evaluated that the productivity of business process modeling is improved by at least 46%. As the conclusion, this paper contributes to prove the effectiveness of the meta-model driven business process modeling methodology for the reuse of business process models.展开更多
目的为提高医院内骨质疏松性骨折(osteoporotic fracture,OF)患者诊疗质量和管理效率,本研究自主构建一种医院内自动抓取相关资料的“骨质疏松性骨折数据库”,数据库内置管理流程相关的智能化功能模块。在此基础上,分析该数据库在实际...目的为提高医院内骨质疏松性骨折(osteoporotic fracture,OF)患者诊疗质量和管理效率,本研究自主构建一种医院内自动抓取相关资料的“骨质疏松性骨折数据库”,数据库内置管理流程相关的智能化功能模块。在此基础上,分析该数据库在实际场景应用的结果和有效性。方法构建院内封闭式多源异构数据整合的专病数据库,数据库接口可后台对接医院的信息系统(hospital information system,HIS)、影像归档和通信系统(picture archiving and communication systems,PACS)、实验室信息系统(laboratory information system,LIS)等固有数据平台,并自动运用自然语言处理(natural language processing,NLP)技术识别及整合OF患者相关信息。运用该数据库纳入2022年6月至2024年6月苏州大学附属第二医院收治的50岁以上、4部位骨折(椎体、髋部、肱骨近端和桡骨远端)的12754例患者,并对患者信息进行智能化管理应用分析。结果该数据库可按照纳入条件自动获得12754例患者数据,并自动收集患者基本资料、病历或影像检查的骨折记录、检验检查结果、实时治疗方案等407个结构化字段信息。数据库可自动完成患者的骨质疏松相关数据识别(骨折部位、骨密度值、骨代谢相关指标、抗骨质疏松药使用)、院内转科及经治医生追踪、院内多次骨折记录检索。当患者确定纳入管理,数据库可实现本次骨折后2年档案构建、辅助宣教、智能随访、院内门诊电脑同屏显示等智能化管理功能。结论“骨质疏松性骨折数据库”拥有便捷的OF患者信息抓取功能,可实时了解相应管理的基础数据,可自动完成规定时间内设定管理的指导及提醒。该数据库有院内多源异构数据整合的专病数据库特点,为OF精准化、智能化、便捷化管理提供新的思路和有效工具。展开更多
An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem....An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.展开更多
文摘Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.
文摘Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models and best practices, we propose a methodology and an integrated environment for business process modeling driven by the metamodel. Furthermore, we propose a process-template design method to unify the granularity and separate the commonality and variability of business processes so that business process models can be reused across different enterprise software systems. The proposed methodology enables to create reuse-oriented business process templates before the business process modeling. To support the proposed methodology, we developed an integrated environment for creating, reusing and verifying the business process models. As the key techniques, we describe the methodology and its integrated environment, including a metamodel and notations. We applied the methodology and integrated environment to an actual enterprise software development project, and evaluated that the productivity of business process modeling is improved by at least 46%. As the conclusion, this paper contributes to prove the effectiveness of the meta-model driven business process modeling methodology for the reuse of business process models.
文摘目的为提高医院内骨质疏松性骨折(osteoporotic fracture,OF)患者诊疗质量和管理效率,本研究自主构建一种医院内自动抓取相关资料的“骨质疏松性骨折数据库”,数据库内置管理流程相关的智能化功能模块。在此基础上,分析该数据库在实际场景应用的结果和有效性。方法构建院内封闭式多源异构数据整合的专病数据库,数据库接口可后台对接医院的信息系统(hospital information system,HIS)、影像归档和通信系统(picture archiving and communication systems,PACS)、实验室信息系统(laboratory information system,LIS)等固有数据平台,并自动运用自然语言处理(natural language processing,NLP)技术识别及整合OF患者相关信息。运用该数据库纳入2022年6月至2024年6月苏州大学附属第二医院收治的50岁以上、4部位骨折(椎体、髋部、肱骨近端和桡骨远端)的12754例患者,并对患者信息进行智能化管理应用分析。结果该数据库可按照纳入条件自动获得12754例患者数据,并自动收集患者基本资料、病历或影像检查的骨折记录、检验检查结果、实时治疗方案等407个结构化字段信息。数据库可自动完成患者的骨质疏松相关数据识别(骨折部位、骨密度值、骨代谢相关指标、抗骨质疏松药使用)、院内转科及经治医生追踪、院内多次骨折记录检索。当患者确定纳入管理,数据库可实现本次骨折后2年档案构建、辅助宣教、智能随访、院内门诊电脑同屏显示等智能化管理功能。结论“骨质疏松性骨折数据库”拥有便捷的OF患者信息抓取功能,可实时了解相应管理的基础数据,可自动完成规定时间内设定管理的指导及提醒。该数据库有院内多源异构数据整合的专病数据库特点,为OF精准化、智能化、便捷化管理提供新的思路和有效工具。
基金This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB0800400, the National Basic Research 973 Program of China under Grant No. 2014CB340404, the National Natural Science Foundation of China under Grant Nos. 61572371, 61273216, and 61272111, the China Postdoctoral Science Foundation （CPSF） under Grant No. 2015M582272, the Natural Science Foundation of Hubei Province of China under Grant No. 2016CFB158, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2042016kf0033.
文摘An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.
