On the understanding of software engineering related texts via the transfer of prior knowledge
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Hadi, Mohammad A
issued: 2023-02-27T22:06:39Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Software Engineering (SE) related natural language texts, such as app reviews and crowdsourced Questions-Answers (Q&A) play pivotal roles for software engineers and developers to gain knowledge regarding different stages of software life-cycle, such as application development, deployment, and maintenance. Successful classification and clustering of these texts can help developers and engineers quickly understand and process the bulk of information in the most effective way. Therefore, this thesis focuses on the efficient classification and clustering of SE-related texts using state-of-the-art neural language models and adaptive topic modeling techniques, respectively. An extensive empirical study is performed to understand the strength, effectiveness, and competence of Pre-trained Transformer based neural language Models (PTM) for the app review classifications task, which also identified the best-performing PTMs. Two of the best-performing PTM models have also been pre-trained from scratch on domain-specific data to yield better classification performance. The largest domain-specific app review dataset has been scraped from Google Play Store for this pretraining purpose. For SE texts clustering purposes, a new online adaptive Topic Model, Adaptive Online Bi-term Topic Model (AOBTM) has been proposed that can efficiently identify topics from corpora sliced over different time and version slices. This topic model leverages and adapts the statistical data inferred in the preceding slices to infer latent topics from the latest slice. The approach yields good results for the short and noisy SE-related natural language texts.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83863
note: On the Understanding of SoftwareEngineering Related Texts via theTransfer of Prior KnowledgebyMohammad A HadiB.Sc. Hons., North South University, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)February 2023© Mohammad A Hadi, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis entitled:On the Understanding of Software Engineering Related Textsvia the Transfer of Prior Knowledgesubmitted byMohammad A Hadi in partial fulfilment of the requirementsof the degree of Master of Science.Examining Committee:Dr. Fatemeh H FardIrving K. Barber Faculty of ScienceSupervisorDr. Yong GaoIrving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Zheng LiuIrving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Anas ChaabanFaculty of Applied ScienceExternal ExamineriiAbstractSoftware Engineering (SE) related natural language texts, such as appreviews and crowdsourced Questions-Answers (Q&A) play pivotal roles forsoftware engineers and developers to gain knowledge regarding differentstages of software life-cycle, such as application development, deployment,and maintenance. Successful classification and clustering of these texts canhelp developers and engineers quickly understand and process the bulk ofinformation in the most effective way. Therefore, this thesis focuses onthe efficient classification and clustering of SE-related texts using state-of-the-art neural language models and adaptive topic modeling techniques,respectively. An extensive empirical study is performed to understand thestrength, effectiveness, and competence of Pre-trained Transformer basedneural language Models (PTM) for the app review classifications task, whichalso identified the best-performing PTMs. Two of the best-performing PTMmodels have also been pre-trained from s…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0427284
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Fard, Fatemeh Hendijani
type: Text
identifier URI: http://hdl.handle.net/2429/83863