Reporting usability defects

Yusop, Nor Shahida Mohamad; Grundy, John; Vasa, Rajesh

doi:10.1145/2915970.2915995

Cited by 25 publications

(5 citation statements)

References 24 publications

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“…Bug report contents have been investigated. Using a questionnaire, one study identified differences between the information shared by reporters and useful information according to developers (Yusop et al, 2016). For example, reporters indicated that titles and summaries are important, whereas developers felt that they are unnecessary.…”

Section: Evaluating Bug Reportsmentioning

confidence: 99%

Deep learning and gradient-based extraction of bug report features related to bug fixing time

Noyori

Washizaki

Fukazawa

et al. 2023

Front. Comput. Sci.

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Bug reports typically contain detailed descriptions of failures, hints at the location of the corresponding defects, and discussions. Developers usually resolve bugs using comments in descriptions and discussions. The time to fix a bug varies greatly. Previous studies have investigated bug reports, but the influence of comments on bug fixing time is not well understood. This study adopts a convolutional neural network (CNN) and gradient-based visualization approach called Grad-cam to elucidate the impact of comments on bug fixing time and extract features. A feature represents an observed characteristic in a bug report when processing via deep learning. Specifically, CNN classifies bug reports, and then Grad-cam visualizes the decision basis of CNN by identifying the top 10 word sequences used in the prediction. Here, the features are major word sequences extracted by Grad-cam. In an experiment, the proposed method classified more than 36,000 actual bug reports from Bugzilla with an accuracy of 75%–80%. Additionally, the visualization highlighted differences in the stack trace and word abstraction by bug fixing time. Bug reports with short bug fixing times are concrete, whereas those with a long bug fixing time are abstract.

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Section: Evaluating Bug Reportsmentioning

confidence: 99%

Deep learning and gradient-based extraction of bug report features related to bug fixing time

Noyori

Washizaki

Fukazawa

et al. 2023

Front. Comput. Sci.

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“…Other papers examine cases where the information provided by the reporter and the information required by the developer conflict [13]. Some methods search for complementing information from missing information in past bug reports [14], [15], while others specialize on bugs affecting usability [16].…”

Section: Related Workmentioning

confidence: 99%

What are the Features of Good Discussions for Shortening Bug Fixing Time?

Noyori

Washizaki

Fukazawa

et al. 2021

IEICE Trans. Inf. & Syst.

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“…There are multiple types of research analyzing the structure of reports containing usability defects, with the purpose of improving the existing format [24], [25], [26]. Yusop et al surveyed practitioners in industrial software organizations and in open source communities about their usability defect reporting practices [24].…”

Section: Related Workmentioning

confidence: 99%

Design-Time Web Usability Evaluation with Guideliner

Marenkov

Robal

Kalja

2018

CSIMQ

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Abstract. The diversity of smartphones and tablet computers has become an integral part of modern life. An essential requirement for web application development is following web usability guidelines, while designing web user interface (UI). Even a minor change in UI could lead to usability problems. Empirical evaluation methods like interviews and questionnaires with user-tests and card sorting are effective in finding such problems. Nevertheless, there are multiple obstacles preventing the application of these methods especially for evaluating minor UI changes, for instance, due to the time and human-resources they require, and the amount of data to be processed. The purpose of this current publication is to present Guideliner -a tool for implementation-time automatic evaluation of web UI conformance to predefined usability guidelines. The main contribution of the presented solution is enabling immediate cost-efficient and automated web UI evaluation that conforms to available and set standards.

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Reporting usability defects

Cited by 25 publications

References 24 publications

Deep learning and gradient-based extraction of bug report features related to bug fixing time

Deep learning and gradient-based extraction of bug report features related to bug fixing time

What are the Features of Good Discussions for Shortening Bug Fixing Time?

Design-Time Web Usability Evaluation with Guideliner

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