Authors:
Leonardo Sampaio Cairo
1
;
Glauco de Figueiredo Carneiro
1
and
Bruno C. da Silva
2
Affiliations:
1
Universidade Salvador (UNIFACS), BA and Brazil
;
2
California Polytechnic State University (Cal Poly), San Luis Obispo, CA and U.S.A.
Keyword(s):
Machine Learning, Text Mining, Systematic Mapping, Systematic Literature Review, Secondary Studies.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Software Engineering
;
Web Information Systems and Technologies
Abstract:
Context: Secondary studies such as systematic literature reviews (SLR) have been used to collect and synthesize empirical evidence from relevant studies in several areas of knowledge, including Computer Science. However, secondary studies are time-consuming and require a significant effort from researchers. Goal: This paper aims to identify contributions derived from the adoption of machine learning (ML) techniques in Computer Science SLRs. Method: We performed a systematic mapping study querying well-known repositories and first found 399 studies as a result of applying the search string in each of the selected search engines. Following the research protocol, we analyzed titles and abstracts and applied inclusion, exclusion and quality criteria to finally obtain a set of 17 studies to be further analyzed. Results: The selected papers provided evidence of relevant contributions of the machine learning usage in performing secondary studies. We found that ML techniques have not been ap
plied yet to all the stages of a SLR. Typically, the preferred stage to apply ML in an SLR is the study selection phase (typically the initial phase). For assessing the effectiveness of ML support while performing SLRs, researchers have provided a comparison either across different ML techniques tested or between manual and ML-supported SLRs. Conclusion: There is significant evidence that the use of machine learning applied to SLR activities (especially the study selection activity) in Computer Science is feasible and promising, and the findings can be potentially extended to other research fields. Also, there is a lack of studies exploring ML techniques for other stages than study selection.
(More)