Publication:
Automated and Improved Search Query Effectiveness Design for Systematic Literature Reviews

dc.contributor.advisor Benatallah, Boualem
dc.contributor.author Badami, Maisie
dc.date.accessioned 2022-07-22T01:44:42Z
dc.date.available 2022-07-22T01:44:42Z
dc.date.issued 2021
dc.date.submitted 2022-07-21T22:18:01Z
dc.description.abstract This research explores and investigates strategies towards automation of the systematic literature review (SLR) process. SLR is a valuable research method that follows a comprehensive, transparent, and reproducible research methodology. SLRs are at the heart of evidence-based research in various research domains, from healthcare to software engineering. They allow researchers to systematically collect and integrate empirical evidence in response to a focused research question, setting the foundation for future research. SLRs are also beneficial to researchers in learning about the state of the art of research and enriching their knowledge of a topic of research. Given their demonstrated value, SLRs are becoming an increasingly popular type of publication in different disciplines. Despite the valuable contributions of SLRs to science, performing timely, reliable, comprehensive, and unbiased SLRs is a challenging endeavour. With the rapid growth in primary research published every year, SLRs might fail to provide complete coverage of existing evidence and even end up being outdated by the time of publication. These challenges have sparked motivation and discussion in research communities to explore automation techniques to support the SLR process. In investigating automatic methods for supporting the systematic review process, this thesis develops three main areas. First, by conducting a systematic literature review, we found the state of the art of automation techniques that are employed to facilitate the systematic review process. Then, in the second study, we identified the real challenges researchers face when conducting SLRs, through an empirical study. Moreover, we distinguished solutions that help researchers to overcome these challenges. We also identified the researchers' concerns regarding adopting automation techniques in SLR practice. Finally, in the third study, we leveraged the findings of our previous studies to investigate a solution to facilitate the SLR search process. We evaluated our proposed method by running some experiments.
dc.identifier.uri http://hdl.handle.net/1959.4/100478
dc.language English
dc.language.iso en
dc.publisher UNSW, Sydney
dc.rights CC BY 4.0
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject.other systematic review
dc.subject.other reinforcement learning
dc.subject.other search quey
dc.subject.other text enrichment
dc.subject.other word embedding
dc.title Automated and Improved Search Query Effectiveness Design for Systematic Literature Reviews
dc.type Thesis
dcterms.accessRights open access
dcterms.rightsHolder Badami, Maisie
dspace.entity.type Publication
unsw.accessRights.uri https://purl.org/coar/access_right/c_abf2
unsw.date.workflow 2022-07-22
unsw.identifier.doi https://doi.org/10.26190/unsworks/24185
unsw.relation.faculty Other UNSW
unsw.relation.faculty Engineering
unsw.relation.school School of Computer Science and Engineering
unsw.subject.fieldofresearchcode 461201 Automated software engineering
unsw.subject.fieldofresearchcode 461202 Empirical software engineering
unsw.thesis.degreetype PhD Doctorate
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