Intelligent Development Environment and Software Knowledge Graph

Lin, Zeqi; Xie, Bing; Zou, Yanzhen; Zhao, Junfeng; Li, Xuandong; Wei, Jun; Sun, Hailong; Yin, Gang

doi:10.1007/s11390-017-1718-y

Cited by 53 publications

(14 citation statements)

References 13 publications

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“…Hay gran cantidad de investigaciones que aplican el GC para representar el conocimiento (Bellomarini et al, 2020;Jia et al, 2017;Krenn & Zeilinger, 2020;Lin et al, 2017;Zhu et al, 2019). La estructura del GC facilita la utilización de ponderaciones que permiten obtener nuevo conocimiento y conclusiones de datos existentes Li & Madden, 2019;.…”

Section: Revisión De La Literaturaunclassified

Knowledge Graph to determine the domain of learning in Higher Education

Almazán

Vega

Amaya

et al. 2021

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The representing of a student’s knowledge in an academic discipline plays an important role in boosting the student’s skills. To support stakeholders in the educational domain, it is necessary to provide them with robust assessment strategies that facilitate the teaching-learning process. Student´s mastery is determined by the degree of knowledge, which demonstrates objectively, on the topics included in the different areas that make up an academic discipline. Although there is a wide variety of techniques to represent knowledge, particularly Knowledge Graph technique is becoming relevant due to the structured approach and benefits it offers. This paper proposes a method that classifies and weights the nodes (topics) of a Knowledge Graph of a disciplinary area, which is analyzed through a case study. The method has two approaches: avoid exhaustive evaluation of the nodes and weight the nodes with adequate precision. Method´s application is illustrated by a case study. As results, a Knowledge Graph is obtained with its classified and weighted nodes through the application of the proposed method, in which 100% of the topics have been impacted through the objective evaluation of 20.8% representing 10 nodes. It is concluded that the proposed method has potential to be used in the representation and management of knowledge, being necessary to improve phases’ iteration to condition number of objective nodes.

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Section: Revisión De La Literaturaunclassified

Knowledge Graph to determine the domain of learning in Higher Education

Almazán

Vega

Amaya

et al. 2021

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“…Lin et al propose a system (called IntelliDE ) in which software big data could be aggregated, mined and analysed to provide meaningful assistance for developers across the software development processes [161]. Similar to CAKI, IntelliDE follows a 3-stage knowledge discovery approach, including Data Aggregation, Knowledge Acquisition and Intelligent Assistant.…”

Section: Mining Architectural Knowledgementioning

confidence: 99%

An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

Osman

Ho-Quang

Chaudron

2018

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Context: In modern software development, software modeling is considered to be an essential part of the software architecture and design activities. The Unified Modeling Language (UML) has become the de facto standard for software modeling in industry. Surprisingly, there are only a few empirical studies on the practices and impacts of UML modeling in software development. This is mainly due to the lack of empirical data on real-life software systems that use UML modeling. Objective: This PhD thesis contributes to this matter by describing a method to build and curate a big corpus of open-source-software (OSS) projects that contain UML models. Subsequently, this thesis offers observations on the practices and impacts of using UML modeling in these OSS projects. Method: We combine techniques from repository mining and image classification in order to successfully identify more than 24.000 open source projects on GitHub that together contain more than 93.000 UML models. Machine learning techniques are also used to enrich the corpus with annotations. Finally, various empirical studies, including a case study, a user study, a large-scale survey and an experiment, have been carried out across this set of projects. Result: The results show that UML is generally perceived to be helpful to new contributors. The most important motivation for using UML seems to be to facilitate collaboration. In particular, teams use UML during communication and planning of joint implementation efforts. Our study also shows that the use of UML modeling has a positive impact on software quality, i.e. it correlates with lower defect proneness. Further, we find out that visualisation of design concepts, such as class role-stereotypes, helps developers to perform better in software comprehension tasks.

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“…The distance between a code element and a document's semantic center. We use Code Embedding approach [14] to map all code elements to a 200-dimension vector space. We get the vectors for all the code elements occurring in a document and average these vectors as the semantic center of this document.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Among these work, the RecoDoc approach [6] obtains the best 96% precision in extracting code elements from software documents in their experiments. However, there are usually more than one or even dozens of code elements in a software document, but not each of them has been introduced sufficiently [7,13,14]. For example, a software document D illustrates how to reuse method m 1 and contains a code usage example and some text segments.…”

Section: Introductionmentioning

confidence: 99%

Toward accurate link between code and software documentation

Cao

Zou

Luo

et al. 2018

Sci. China Inf. Sci.

Self Cite

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Recovering traceability links between source code and software documentation is an important research topic in software maintenance and software reuse. There have been a lot of research efforts in recovering traceability between documentation and code elements (class, interface, method, etc.), mostly based on program analysis. However, there are still a lot of noise links being established in existing work. In this paper, we propose a novel approach to classifying code elements, occurring in a document, into contextual code elements and salient code elements. As a result, we can filter the noise traceability links between a software document and its contextual code elements and get a higher quality link set. Our classifier is trained based on open source project Lucene's source code and 1899 StackOverflow answer documents about Lucene. We extract code elements from these documents and represent each of these code elements with a 7-dimension feature vector, then we use a decision-tree-based learning model to classify them as salient or not. In the experiments, we get a precision of 70.7% in recognizing the salient code elements of these documents and get 12% improvement compared with Rigby's work. We can filter out 56.5%∼69.3% noise traceability links with different thresholds in our classifier. It can improve the quality of traceability links between source code and their related software documents in application.

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Intelligent Development Environment and Software Knowledge Graph

Cited by 53 publications

References 13 publications

Knowledge Graph to determine the domain of learning in Higher Education

Knowledge Graph to determine the domain of learning in Higher Education

An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

Toward accurate link between code and software documentation

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