English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the most important tasks related to managing bug reports is localizingthe fault so that a fix can be applied. As such, prior work has aimed toautomate this task of bug localization by formulating it as an informationretrieval problem, where potentially buggy files are retrieved and rankedaccording to their textual similarity with a given bug report. However, thereis often a notable semantic gap between the information contained in bugreports and identifiers or natural language contained within source code files.For user-facing software, there is currently a key source of information thatcould aid in bug localization, but has not been thoroughly investigated -information from the GUI.  We investigate the hypothesis that, for end user-facing applications,connecting information in a bug report with information from the GUI, and usingthis to aid in retrieving potentially buggy files, can improve upon existingtechniques for bug localization. To examine this phenomenon, we conduct acomprehensive empirical study that augments four baseline techniques for buglocalization with GUI interaction information from a reproduction scenario to(i) filter out potentially irrelevant files, (ii) boost potentially relevantfiles, and (iii) reformulate text-retrieval queries. To carry out our study, wesource the current largest dataset of fully-localized and reproducible realbugs for Android apps, with corresponding bug reports, consisting of 80 bugreports from 39 popular open-source apps. Our results illustrate thataugmenting traditional techniques with GUI information leads to a markedincrease in effectiveness across multiple metrics, including a relativeincrease in Hits@10 of 13-18%. Additionally, through further analysis, we findthat our studied augmentations largely complement existing techniques.

On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization