3839075
doi
10.5281/zenodo.3839075
oai:zenodo.org:3839075
user-empirical-software-engineering
Bradley, Nick C.
University of British Columbia
Murphy, Gail C.
University of British Columbia
Characterizing Task-Relevant Information in Natural Language Software Artifacts
Marques, Arthur
University of British Columbia
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Contains the supplementary material for the paper "Characterizing Task-Relevant Information<br>
in Natural Language Software Artifacts". All contents are explained in the file README.md.</p>
<p> </p>
<p><strong>Abstract:</strong> To complete a software development task, a software developer often consults artifacts that contain largely natural language text, such as API documentation, bug reports, or Q&A forums. Not all information within these artifacts is relevant to a developer's current task forcing the developer to filter relevant information from large amounts of irrelevant information, a frustrating and time-consuming activity. Since failing to locate relevant information may lead to incorrect or incomplete solutions, many approaches mine potentially relevant text from such natural language artifacts. However, existing approaches identify text relevant for only certain categories of tasks (e.g., learning an API) and from a restricted set of artifact types. To explore how limitations on software development tasks and artifact types can be relaxed in future approaches, we conducted an experiment in which 20 participants identified which text appearing in 1874 sentences across 20 artifacts was relevant to six software development tasks. Participants created 2,463 distinct highlights in these sentences to indicate relevance. Although the results indicate variability in the text perceived as relevant, we observe consistency in the information considered <em>key</em> for task completion. The semantic meaning of relevant information, as identified through semantic frames, shows promise to automate the identification of relevant text. We discuss implications of our study for future research in the field.</p>
Zenodo
2020-05-22
info:eu-repo/semantics/article
3839074
user-empirical-software-engineering
1
1623425243.340629
2684954
md5:8f3d9cc6dabf60b095381336517c839d
https://zenodo.org/records/3839075/files/TaskRelevantInfo_SupplementalMaterial.zip
public
10.5281/zenodo.3839074
isVersionOf
doi