Analysing the use of graphs to represent the results of systematic reviews in software engineering

Date
2011
Authors
Felizardo, KR,
Riaz, M,
Sulayman, M,
Mendes, E
MacDonell, SG
Maldonaldo, JC
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Computer Society Press
Abstract

The presentation of results from Systematic Literature Reviews (SLRs) is generally done using tables. Prior research suggests that results summarized in tables are often difficult for readers to understand. One alternative to improve results' comprehensibility is to use graphical representations. The aim of this work is twofold: first, to investigate whether graph representations result is better comprehensibility than tables when presenting SLR results; second, to investigate whether interpretation using graphs impacts on performance, as measured by the time consumed to analyse and understand the data. We selected an SLR published in the literature and used two different formats to represent its results - tables and graphs, in three different combinations: (i) table format only; (ii) graph format only; and (iii) a mixture of tables and graphs. We conducted an experiment that compared the performance and capability of experts in SLR, as well as doctoral and masters students, in analysing and understanding the results of the SLR, as presented in one of the three different forms. We were interested in examining whether there is difference between the performance of participants using tables and graphs. The graphical representation of SLR data led to a reduction in the time taken for its analysis, without any loss in data comprehensibility. For our sample the analysis of graphical data proved to be faster than the analysis of tabular data. However, we found no evidence of a difference in comprehensibility whether using tables, graphical format or a combination. Overall we argue that graphs are a suitable alternative to tables when it comes to representing the results of an SLR.

Description
Keywords
Systematic Literature Review , Visual Text Mining , Computer science , Context , Data visualization , Educational institutions , Software engineering , Systematics , Visualization
Source
Proceedings of the 25th Brazilian Symposium on Software Engineering (SBES 2011), São Paulo, Brazil, pp. 174 - 183
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