[Zurück]

A. Neto, M. Kalinowski, A. Garcia, D. Winkler, S. Biffl:
"A Preliminary Comparison of Using Variability Modeling Approaches to Represent Experiment Families";
Vortrag: EASE 2019- Evaluation and Assessment in Software Engineering, Copenhagen, Denmark; 14.04.2019 - 17.04.2019; in: "Proceedings of the Evaluation and Assessment on Software Engineering", ACM, (2019), ISBN: 978-1-4503-7145-2; S. 333 - 338.

Replication is essential to build knowledge in
empirical science. Replications reported in the software
engineering context present variabilities on experiment design
elements (e.g., variables, materials). Further understanding these
variabilities could help planning experimental replications within a
research program. Aims: To explore the possibility of using
Variability Modeling Approaches (VMAs) to represent experiment
families and the feasibility of using experiment lines in order to
support experiment replications. Method: We selected two families
of experiments and analyzed their variabilities. Thereafter, we
represented the corresponding experiment lines using the following
VMAs: Feature Model, Decision Model and Orthogonal
Variability Model. Based on these models, we conducted a
preliminary comparison to analyze and discuss the advantages and
disadvantages of using them to support replication planning.
Results: We were able to represent the variabilities within the
experiment family using the VMAs. The resulting models can be
used to support experiment replication planning, helping to
understand opportunities for further investigation within and
beyond a research group. Conclusions: Using experiment lines to
support planning experiment replications is a promising direction
of research and should be further investigated.

Experiment planning, experiment replication, experiment lines

http://dx.doi.org/10.1145/3319008.3319356