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Authors: Zaki Pauzi and Andrea Capiluppi

Affiliation: Bernoulli Institute, University of Groningen, The Netherlands

Keyword(s): Natural Language Processing, Software Traceability, Time Series, Systematic Review.

Abstract: Systematic literature reviews (SLRs) and systematic mapping studies (SMSs) are common studies in any disci- pline to describe and classify past works, and to inform a research field of potential new areas of investigation. This last task is typically achieved by observing gaps in past works, and hinting at the possibility of future re- search in those gaps. Using an NLP-driven methodology, this paper proposes a meta-analysis to extend current systematic methodologies of literature reviews and mapping studies. Our work leverages a Word2Vec model, pre-trained in the software engineering domain, and is combined with a time series analysis. Our aim is to forecast future trajectories of research outlined in systematic studies, rather than just describing them. Using the same dataset from our own previous mapping study, we were able to go beyond descriptively analysing the data that we gathered, or to barely ‘guess’ future directions. In this paper, we show how recent advancements in the f ield of our SMS, and the use of time series, enabled us to forecast future trends in the same field. Our proposed methodology sets a precedent for exploring the potential of language models coupled with time series in the context of systematically reviewing the literature. (More)

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Paper citation in several formats:
Pauzi, Z. and Capiluppi, A. (2023). From Descriptive to Predictive: Forecasting Emerging Research Areas in Software Traceability Using NLP from Systematic Studies. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 538-545. DOI: 10.5220/0011964100003464

@conference{enase23,
author={Zaki Pauzi and Andrea Capiluppi},
title={From Descriptive to Predictive: Forecasting Emerging Research Areas in Software Traceability Using NLP from Systematic Studies},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2023},
pages={538-545},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011964100003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - From Descriptive to Predictive: Forecasting Emerging Research Areas in Software Traceability Using NLP from Systematic Studies
SN - 978-989-758-647-7
IS - 2184-4895
AU - Pauzi, Z.
AU - Capiluppi, A.
PY - 2023
SP - 538
EP - 545
DO - 10.5220/0011964100003464
PB - SciTePress