Reference : Experience-based guidelines for effective and efficient data extraction in systematic...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/31046
Experience-based guidelines for effective and efficient data extraction in systematic reviews in software engineering
English
Garousi, Vahid mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Felderer, Michael mailto []
Jun-2017
Proceedings of International Conference on Evaluation and Assessment in Software Engineering (EASE)
ACM
170-179
Yes
Yes
International
International Conference on Evaluation and Assessment in Software Engineering (EASE)
June 2017
Karlskrona
Sweden
[en] Systematic mapping studies ; systematic literature reviews ; SLR ; research methodology ; SM ; data extraction ; empirical software engineering
[en] To systematically collect evidence and to structure a given area in software engineering (SE), Systematic Literature Reviews (SLR) and Systematic Mapping (SM) studies have become common. Data extraction is one of the main phases (activities) when conducting an SM or an SLR, whose objective is to extract required data from the primary studies and to accurately record the information researchers need to answer the questions of the SM/SLR study. Based on experience in a large number of SM/SLR studies, we and many other researchers have found the data extraction in SLRs to be time consuming and error-prone, thus raising the real need for heuristics and guidelines for effective and efficient data extraction in these studies, especially to be learnt by junior and young researchers. As a ‘guideline’ paper, this paper contributes a synthesized list of challenges usually faced during SLRs’ data extraction phase and the corresponding solutions (guidelines). For our synthesis, we consider two data sources: (1) the pool of 16 SLR studies in which the authors have been involved in, as well as (2) a review of challenges and guidelines in the existing literature. Our experience in utilizing the presented guidelines in the near past have helped our junior colleagues to conduct data extractions more effectively and efficiently.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
National Research Fund, Luxembourg FNR/P10/03
National Research Fund, Luxembourg FNR/P10/03
Researchers ; Professionals
http://hdl.handle.net/10993/31046
10.1145/3084226.3084238
FnR ; FNR3949772 > Lionel Briand > VVLAB > Validation And Verification Laboratory > 01/01/2012 > 31/07/2018 > 2010

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
EASE 2017-Guidelines for data extraction-May 5.pdfAuthor preprint612.03 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.