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research-article

Topic modeling in software engineering research

Authors:

Camila Costa Silva,

Matthias Galster,

Fabian GilsonAuthors Info & Claims

Empirical Software Engineering, Volume 26, Issue 6

https://doi.org/10.1007/s10664-021-10026-0

Published: 01 November 2021 Publication History

Abstract

Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common.

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cover image Empirical Software Engineering

Empirical Software Engineering Volume 26, Issue 6

Nov 2021

1006 pages

ISSN:1382-3256

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Kluwer Academic Publishers

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Published: 01 November 2021

Accepted: 29 July 2021

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Cited By

Ozyurt HOzyurt O(2025)Decoding educational augmented reality research trends: a topic modeling analysisEducation and Information Technologies10.1007/s10639-024-12943-130:1(57-87)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10639-024-12943-1
Unterkalmsteiner MBadampudi DBritto RAli NDig DBryksin TGolubev YBezzubov A(2024)Help Me to Understand this Commit! - A Vision for Contextualized Code ReviewsProceedings of the 1st ACM/IEEE Workshop on Integrated Development Environments10.1145/3643796.3648447(18-23)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3643796.3648447
Espinosa JMateus SRamirez D(2024)Text Analysis Software Using Topic Modeling Techniques for the Extraction of Knowledge from Cases Related to Vulnerability and Access to JusticeArtificial Intelligence in HCI10.1007/978-3-031-60615-1_23(334-352)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60615-1_23
Kotti ZGalanopoulou RSpinellis D(2023)Machine Learning for Software Engineering: A Tertiary StudyACM Computing Surveys10.1145/357290555:12(1-39)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3572905
Lupi FMabkhot MBoffa EFerreira PAntonelli DMaffei ALohse NLanzetta M(2023)Automatic definition of engineer archetypesComputers in Industry10.1016/j.compind.2023.103996152:COnline publication date: 1-Nov-2023
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Hadi MFard F(2023)Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviewsEmpirical Software Engineering10.1007/s10664-023-10314-x28:4Online publication date: 23-May-2023
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Han YWang ZFeng YZhao ZWang Y(2023)Cross-status communication and project outcomes in OSS developmentEmpirical Software Engineering10.1007/s10664-023-10298-828:3Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10298-8
Gurcan FErdogdu FCagiltay NCagiltay K(2023)Student engagement research trends of past 10 years: A machine learning-based analysis of 42,000 research articlesEducation and Information Technologies10.1007/s10639-023-11803-828:11(15067-15091)Online publication date: 1-Nov-2023
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Alfayez RWinn RDing YAlfayez GBoehm B(2023)Technical debt (TD) through the lens of TwitterJournal of Software: Evolution and Process10.1002/smr.254736:4Online publication date: 4-Mar-2023
https://dl.acm.org/doi/10.1002/smr.2547

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