Strategies and behaviors of end-user programmers with interactive fault localization

Prabhakararao, S.; Cook, Curtis R.; Ruthruff, Joseph R.; Creswick, Eugene R.; Main, Martin B.; Durham, Michael D.; Burnett, Margaret

doi:10.1109/hcc.2003.1260197

Cited by 28 publications

(27 citation statements)

References 21 publications

(21 reference statements)

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“…Second, the quick improvements-during even the first 10 user actionstreatment participants made to their classifiers suggest that users will remain engaged with Explanatory Debugging. This matters because research has shown that if an end-user debugging technique is not perceived as useful after a small number of interactions, users are unlikely to continue using it [32].…”

Section: Elucidebug's Explanations To End Usersmentioning

confidence: 99%

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza

Burnett

Wong

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

393

366

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How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system.

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Section: Elucidebug's Explanations To End Usersmentioning

confidence: 99%

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza

Burnett

Wong

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

393

366

View full text Add to dashboard Cite

show abstract

“…Prabhakararao and Ruthruff et al performed two human studies to gauge the effectiveness of an interactive fault localization tool developed for end users with little to no experience [21], [24]. The goal of their studies was to evaluate the use of feedback when locating faults and to generally study the process of fault localization, especially by users with no expert domain knowledge of the source.…”

Section: Related Workmentioning

confidence: 99%

A human study of fault localization accuracy

Fry

Weimer

2010

2010 IEEE International Conference on Software Maintenance

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Abstract-Localizing and repairing defects are critical software engineering activities. Not all programs and not all bugs are equally easy to debug, however. We present formal models, backed by a human study involving 65 participants (from both academia and industry) and 1830 total judgments, relating various software-and defect-related features to human accuracy at locating errors. Our study involves example code from Java textbooks, helping us to control for both readability and complexity. We find that certain types of defects are much harder for humans to locate accurately. For example, humans are over five times more accurate at locating "extra statements" than "missing statements" based on experimental observation. We also find that, independent of the type of defect involved, certain code contexts are harder to debug than others. For example, humans are over three times more accurate at finding defects in code that provides an array abstraction than in code that provides a tree abstraction. We identify and analyze code features that are predictive of human fault localization accuracy. Finally, we present a formal model of debugging accuracy based on those source code features that have a statistically significant correlation with human performance.

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“…Oftentimes, spreadsheets seeded with errors are used in empirical studies to evaluate the effectiveness or usability of errorprevention or -detection mechanisms [40], [41]. The seeding of errors is usually done manually on the basis of accepted classifications [7], [42].…”

Section: Introductionmentioning

confidence: 99%

Mutation Operators for Spreadsheets

Abraham

Erwig

2009

IIEEE Trans. Software Eng.

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Abstract-Based on 1) research into mutation testing for general-purpose programming languages and 2) spreadsheet errors that have been reported in the literature, we have developed a suite of mutation operators for spreadsheets. We present an evaluation of the mutation adequacy of definition-use adequate test suites generated by a constraint-based automatic test-case generation system we have developed in previous work. The results of the evaluation suggest additional constraints that can be incorporated into the system to target mutation adequacy. In addition to being useful in mutation testing of spreadsheets, the operators can be used in the evaluation of error-detection tools and also for seeding spreadsheets with errors for empirical studies. We describe two case studies where the suite of mutation operators helped us carry out such empirical evaluations. The main contribution of this paper is a suite of mutation operators for spreadsheets that can be used for performing empirical evaluations of spreadsheet tools to indicate ways in which the tools can be improved.

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Strategies and behaviors of end-user programmers with interactive fault localization

Cited by 28 publications

References 21 publications

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

A human study of fault localization accuracy

Mutation Operators for Spreadsheets

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