A visual programming paradigm for abstract deep learning model development

Tamilselvam, Srikanth; Panwar, Naveen; Khare, Shreya; Aralikatte, Rahul; Sankaran, Anush; Mani, Senthil

doi:10.1145/3364183.3364202

Cited by 16 publications

(15 citation statements)

References 8 publications

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“…Thus, the engineer requires support to precisely specify the neural network's key-properties. Most papers talking about specifications present domain-specific languages [27][28][29][30] for the construction or the execution of neural networks. Mostly, they present domain-specific languages for the specification of an architecture of a neural network facilitate the implementation.…”

Section: Discussionmentioning

confidence: 99%

Specifying Key-properties to Improve the Recognition Skills of Neural Networks

Jahic

Guelfi

Ries

2020

Proceedings of the 2020 European Symposium on Software Engineering

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Section: Discussionmentioning

confidence: 99%

Specifying Key-properties to Improve the Recognition Skills of Neural Networks

Jahic

Guelfi

Ries

2020

Proceedings of the 2020 European Symposium on Software Engineering

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“…The abstractions are often represented as visual components that end users can drag and drop. The abstractions allow end users to incorporate into their applications: functions [32], [51], web services [43] or software modules [41]. Abstractions are used to increase tool accessibility though this may be at the cost of expressiveness as the abstractions are often designed to operate in a restricted way.…”

Section: First Research Question Dimensionsmentioning

confidence: 99%

“…2) DIAGRAM-BASED TOOLS Tools using the diagram-based approach empower users to construct a program by connecting together visual components where the output of a component serves as a data input to another component. In some tools such as [41], the visual components may represent data sets and algorithms, whereas in other tools such as [42], the visual components simply represent graphical components. Further, the user can configure these services by setting its properties.…”

Section: ) Form-based Toolsmentioning

confidence: 99%

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

et al. 2021

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Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected articles discussed tools that target IoT and education, while other fields such as data science, robotics are emerging. Further, most tools use abstractions to hide implementation details and use similar interaction styles. The predominant platforms for the tools are web and mobile, while desktop-based tools are on the decline. Only a few tools were evaluated with a formal experiment, whilst the remaining ones were evaluated with evaluation studies or informal feedback. Most tools were evaluated with students with little to no programming skills. There is a lack of emphasis on usability principles in the design stage of the tools. Additionally, only one of the tools was evaluated for expressiveness. Other areas for exploration include supporting end users throughout the life cycle of applications created with the tools, studying the impact of tutorials on improving learnability, and exploring the potential of machine learning to improve debugging solutions developed with visual programming.INDEX TERMS Visual programming, end-user development, human-computer interaction, systematic literature review.

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“…It requires an understanding of complex algorithms and working processes, as well as a constantly increasing zoo of architectures, frameworks, etc., which makes choosing a suitable one a difficult task for novices (Gillies, 2016;Gutosk, 2017;Sulmont et al, 2019) as well as requiring the user to have a certain level of programming skills (Xie et al, 2019). As a consequence, students typically face several difficulties when starting to learn ML, making the process of building ML models inaccessible to many people (Ramos et al, 2020;Sankaran et al, 2018;Tamilselvam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

et al. 2021

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Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students’ understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.

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A visual programming paradigm for abstract deep learning model development

Cited by 16 publications

References 8 publications

Specifying Key-properties to Improve the Recognition Skills of Neural Networks

Specifying Key-properties to Improve the Recognition Skills of Neural Networks

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

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