Does continuous visual feedback aid debugging in direct-manipulation programming systems?

Wilcox, Eric; Atwood, J. William; Burnett, Margaret; Cadiz, JJ; Cook, Curtis R.

doi:10.1145/258549.258721

Cited by 57 publications

(35 citation statements)

References 12 publications

(1 reference statement)

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“…About 20 percent of the participants had lower perceived post-self-efficacy scores than pre-self-efficacy scores, which makes this explanation plausible. In addition, overconfidence has been reported in end-user debugging of spreadsheets [51]. We hope to carry out another study to see if this effect can be replicated.…”

Section: Discussionmentioning

confidence: 84%

Factors affecting the success of non-majors in learning to program

Wiedenbeck

2005

Proceedings of the 2005 International Workshop on Computing Education Research - ICER '05

125

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The introductory programming course is difficult for many university students, especially students who have little prior exposure to programming. Many factors affecting student success have been identified, but there is still a dearth of knowledge about how key factors combine to affect course outcomes. In this study we develop and empirically test a model integrating three factors of importance in learning to program: previous programming experience, perceived self-efficacy, and knowledge organization. The participants were non-majors. The findings showed that perceived self-efficacy increased significantly during a semester course. Previous experience affected perceived self-efficacy but not knowledge organization. Both perceived self-efficacy and knowledge organization had an effect on the course grade, as well as on success in a specific programming task, debugging. The results on self-efficacy also suggested that the participants were overconfident about their programming capabilities. The contribution of this paper is the identification of the joint effects of an important set of factors for programming success by nonmajors.

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Section: Discussionmentioning

confidence: 84%

Factors affecting the success of non-majors in learning to program

Wiedenbeck

2005

Proceedings of the 2005 International Workshop on Computing Education Research - ICER '05

125

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“…Solstice removes the burden of creating and maintaining the copy codebase, and relying on external operations from such impure analyses. Similarly, live programming focuses on providing continuous feedback to developers for the tasks they are working on [3,33] by making the behavior of a program visible as that program is being developed [7]. Solstice enables continuous analyses that facilitate live programming practices.…”

Section: Related Workmentioning

confidence: 99%

Making offline analyses continuous

Muşlu

Brun

Ernst

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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Developers use analysis tools to help write, debug, and understand software systems under development. A developer's change to the system source code may affect analysis results. Typically, to learn those effects, the developer must explicitly initiate the analysis. This may interrupt the developer's workflow and/or the delay until the developer learns the implications of the change. The situation is even worse for impure analyses -ones that modify the code on which it runs -because such analyses block the developer from working on the code. This paper presents Codebase Replication, a novel approach to easily convert an offline analysis -even an impure one -into a continuous analysis that informs the developer of the implications of recent changes as quickly as possible after the change is made. Codebase Replication copies the developer's codebase, incrementally keeps this copy codebase in sync with the developer's codebase, makes that copy codebase available for offline analyses to run without disturbing the developer and without the developer's changes disturbing the analyses, and makes analysis results available to be presented to the developer.We have implemented Codebase Replication in Solstice, an opensource, publicly-available Eclipse plug-in. We have used Solstice to convert three offline analyses -FindBugs, PMD, and unit testing -into continuous ones. Each conversion required on average 436 NCSL and took, on average, 18 hours. Solstice-based analyses experience no more than 2.5 milliseconds of runtime overhead per developer action.

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“…As early as early 90's, Singh et al [12] proposed the Sage system that can capture and store GUI interactions demonstrated by users as reusable templates. Wilcox et al [16] illustrated the value of visual feedback in programming by demonstration tools especially during the testing process, a finding validates the design decision of Sikuli Test to embed visual feedback directly in test scripts. Given the popularity of Web-based applications, the Koala system by Little et al [8] and the CoScripter system by Leshed et al [7] both aim to enable Web users to capture, share, automate, and personalize business processes.…”

Section: Related Workmentioning

confidence: 78%

GUI testing using computer vision

Chang

Yeh

Miller

2010

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

166

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Testing a GUI's visual behavior typically requires human testers to interact with the GUI and to observe whether the expected results of interaction are presented. This paper presents a new approach to GUI testing using computer vision for testers to automate their tasks. Testers can write a visual test script that uses images to specify which GUI components to interact with and what visual feedback to be observed. Testers can also generate visual test scripts by demonstration. By recording both input events and screen images, it is possible to extract the images of components interacted with and the visual feedback seen by the demonstrator, and generate a visual test script automatically. We show that a variety of GUI behavior can be tested using this approach. Also, we show how this approach can facilitate good testing practices such as unit testing, regression testing, and test-driven development.

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Does continuous visual feedback aid debugging in direct-manipulation programming systems?

Cited by 57 publications

References 12 publications

Factors affecting the success of non-majors in learning to program

Factors affecting the success of non-majors in learning to program

Making offline analyses continuous

GUI testing using computer vision

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