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A Machine Learning Approach for Semi-Automated Search and Selection in Literature Studies

Ros, Rasmus LU ; Bjarnason, Elizabeth LU and Runeson, Per LU (2017) 21st International Conference on Evaluation and Assessment in Software Engineering (EASE'17) In EASE'17 Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering
Abstract
Background. Search and selection of primary studies in Systematic Literature Reviews (SLR) is labour intensive, and hard to replicate and update. Aims. We explore a machine learning approach to support semi-automated search and selection in SLRs to address these weaknesses. Method. We 1) train a classi er on an initial set of papers, 2) extend this set of papers by automated search and snowballing, 3) have the researcher validate the top paper, selected by the classi er, and 4) update the set of papers and iterate the process until a stopping criterion is met. Results. We demonstrate with a proof-of-concept tool that the proposed automated search and selection approach generates valid search strings and that the performance for subsets of... (More)
Background. Search and selection of primary studies in Systematic Literature Reviews (SLR) is labour intensive, and hard to replicate and update. Aims. We explore a machine learning approach to support semi-automated search and selection in SLRs to address these weaknesses. Method. We 1) train a classi er on an initial set of papers, 2) extend this set of papers by automated search and snowballing, 3) have the researcher validate the top paper, selected by the classi er, and 4) update the set of papers and iterate the process until a stopping criterion is met. Results. We demonstrate with a proof-of-concept tool that the proposed automated search and selection approach generates valid search strings and that the performance for subsets of primary studies can reduce the manual work by half. Conclusions. Thee approach is promising and the demonstrated advantages include cost savings and replicability. e next steps include further tool development and evaluate the approach on a complete SLR. (Less)
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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
EASE'17 Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering
publisher
ACM
conference name
21st International Conference on Evaluation and Assessment in Software Engineering (EASE'17)
external identifiers
  • scopus:85025458249
ISBN
978-1-4503-4804-1
DOI
10.1145/3084226.3084243
language
English
LU publication?
yes
id
67e4b927-201e-4bca-858b-49f8ad7b42b8
date added to LUP
2017-06-21 15:05:41
date last changed
2017-08-06 05:22:45
@inproceedings{67e4b927-201e-4bca-858b-49f8ad7b42b8,
  abstract     = {Background. Search and selection of primary studies in Systematic Literature Reviews (SLR) is labour intensive, and hard to replicate and update. Aims. We explore a machine learning approach to support semi-automated search and selection in SLRs to address these weaknesses. Method. We 1) train a classi er on an initial set of papers, 2) extend this set of papers by automated search and snowballing, 3) have the researcher validate the top paper, selected by the classi er, and 4) update the set of papers and iterate the process until a stopping criterion is met. Results. We demonstrate with a proof-of-concept tool that the proposed automated search and selection approach generates valid search strings and that the performance for subsets of primary studies can reduce the manual work by half. Conclusions. Thee approach is promising and the demonstrated advantages include cost savings and replicability.  e next steps include further tool development and evaluate the approach on a complete SLR.},
  author       = {Ros, Rasmus and Bjarnason, Elizabeth and Runeson, Per},
  booktitle    = {EASE'17 Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering },
  isbn         = {978-1-4503-4804-1 },
  language     = {eng},
  month        = {06},
  publisher    = {ACM},
  title        = {A Machine Learning Approach for Semi-Automated Search and Selection in Literature Studies},
  url          = {http://dx.doi.org/10.1145/3084226.3084243 },
  year         = {2017},
}