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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Characterizing Design Discussions With Semi-Supervised Topic Modeling

Characterizing Design Discussions With Semi-Supervised Topic Modeling

Abstract

Note: Please refer to the README.md file for instructions. Abstract: Stack Overflow is a rich source of questions and answers—discussions—about software development. One topic of discussion is software design, such as the correct use of design patterns, or best practices in data access. Since design is a more abstract topic in software engineering, researchers have long sought to characterize and model design knowledge. However, these approaches typically require significant expert input in order to contextualize the abstract design information. In this study, we explore how combining expert input with Stack Overflow might serve as an effective way to identify design topics. We first perform a qualitative analysis of design-tagged Stack Overflow questions and answers to identify the design concepts developers discuss. We report on areas where agreement was a challenge, including abstraction levels. Since inductive coding is expensive, we apply a semi-supervised (Anchored CorEx) approach. We find it performs as well as LDA but offers superior interpretability and the ability to guide the topic model. We leverage CorEx to characterize how design is discussed in Stack Overflow and on GitHub. We conclude by describing how our experience using the semi-supervised CorEx approach leads us to believe that approaches like CorEx that combine domain knowledge and scalability are key for analyzing large SE text repositories.

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visibility
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
0
Average
Average
Average
2