Yestercode: Improving code-change support in visual dataflow programming environments

Henley, Austin Z.; Fleming, Scott

doi:10.1109/vlhcc.2016.7739672

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…From our prior work, we have found that it is vital for the tool to work without upfront configuration and without disruption to their workflow. For example, Yestercode and CodeDeviant record code changes automatically and provide visualizations that are available if the programmer needs assistance, without upfront configuration [21], [22]. If our inquisitive editor is too disruptive, the programmer will be less likely to use it and it may interfere with their task, and thus contradict the goals we are trying to achieve.…”

Section: A Identifying Misconceptionsmentioning

confidence: 99%

An Inquisitive Code Editor for Addressing Novice Programmers' Misconceptions of Program Behavior

Henley

Ball

Klein

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Novice programmers face numerous barriers while attempting to learn how to code that may deter them from pursuing a computer science degree or career in software development. In this work, we propose a tool concept to address the particularly challenging barrier of novice programmers holding misconceptions about how their code behaves. Specifically, the concept involves an inquisitive code editor that: (1) identifies misconceptions by periodically prompting the novice programmer with questions about their program's behavior, (2) corrects the misconceptions by generating explanations based on the program's actual behavior, and (3) prevents further misconceptions by inserting test code and utilizing other educational resources. We have implemented portions of the concept as plugins for the Atom code editor and conducted informal surveys with students and instructors. Next steps include deploying the tool prototype to students enrolled in introductory programming courses.

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Section: A Identifying Misconceptionsmentioning

confidence: 99%

An Inquisitive Code Editor for Addressing Novice Programmers' Misconceptions of Program Behavior

Henley

Ball

Klein

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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“…In fact, it has been noted that visual data flow programming is most successful where data manipulation is the foremost important task [32]. This visual metaphor is now receiving renewed attention across a range of applications [33,34]. However, within a data science context, these visual data science data flow tools do not focus on the development of R code.…”

Section: Data Science Development Environmentsmentioning

confidence: 99%

MIDST: an enhanced development environment that improves the maintainability of a data science analysis

Saltz

Crowston

Heckman

et al. 2021

IJISPM

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With the increasing ability to generate actionable insight from data, the field of data science has seen significant growth. As more teams develop data science solutions, the analytical code they develop will need to be enhanced in the future, by an existing or a new team member. Thus, the importance of being able to easily maintain and enhance the code required for an analysis will increase. However, to date, there has been minimal research on the maintainability of an analysis done by a data science team. To help address this gap, data science maintainability was explored by (1) creating a data science maintainability model, (2) creating a new tool, called MIDST (Modular Interactive Data Science Tool), that aims to improve data science maintainability, and then (3) conducting a mixed method experiment to evaluate MIDST. The new tool aims to improve the ability of a team member to update and rerun an existing data science analysis by providing a visual data flow view of the analysis within an integrated code and computational environment. Via an analysis of the quantitative and qualitative survey results, the experiment found that MIDST does help improve the maintainability of an analysis. Thus, this research demonstrates the importance of enhanced tools tohelp improve the maintainability of data science projects.

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“…Working with variants is a common phenomenon in exploratory, creative tasks. Researchers have built several tools to support variants in both non-programming (such as graphic design [11,12,45], personal information management [16]) as well as programming domains (e.g., web design [18,20], professional software development [6], end-user programming [22,13]). However, none of these tools (except [13]) support variations in exploratory programming.…”

Section: Rq2: How Effective Is Our New Computational Model? Backgrounmentioning

confidence: 99%

Pfis-V

Ragavan

Pandya

Piorkowski

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Foraging among similar variants of the same artifact is a common activity, but computational models of Information Foraging Theory (IFT) have not been developed to take such variants into account. Without being able to computationally predict people's foraging behavior with variants, our ability to harness the theory in practical ways-such as building and systematically assessing tools for people who forage different variants of an artifact-is limited. Therefore, in this paper, we introduce a new predictive model, PFIS-V, that builds upon PFIS3, the most recent of the PFIS family of modeling IFT in programming situations. Our empirical results show that PFIS-V is up to 25% more accurate than PFIS3 in predicting where a forager will navigate in a variationed information space.

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Yestercode: Improving code-change support in visual dataflow programming environments

Cited by 11 publications

References 28 publications

An Inquisitive Code Editor for Addressing Novice Programmers' Misconceptions of Program Behavior

An Inquisitive Code Editor for Addressing Novice Programmers' Misconceptions of Program Behavior

MIDST: an enhanced development environment that improves the maintainability of a data science analysis

Pfis-V

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