Systematic literature reviews are a central part of evidence-based research. These reviews involve collecting and integrating empirical evidence on specific research questions.
A key step in this process is building Boolean search queries, which are at the core of information retrieval systems that support literature search. This involves turning general research aims into specific search terms that can be combined into complex Boolean expressions. Researchers must build and refine search queries to ensure they have sufficient coverage and properly represent the literature.
In this DCU research collaboration, we propose a new SLR search method that uses deep machine learning to make the best modifications to a search based on feedback from researchers about its performance. Empirical evaluations with 10 SLR datasets showed our approach achieves comparable performance to queries manually composed by SLR authors.