by Stefan Fischer, Lukas Linsbauer, Alexander Egyed, Roberto E. Lopez-Herrejon
Abstract:
Robust and effective support for the detection and management of software features and their interactions is crucial for many development tasks but has proven to be an elusive goal despite extensive research on the subject. This is especially challenging for variable systems where multiple variants of a system and their features must be collectively considered. Here an important issue is the typically large number of feature interactions that can occur in variable systems. We propose a method that computes, from a set of known source code level interactions of n features, the relevant interactions involving n+1 features. Our method is based on the insight that, if a set of features interact, it is much more likely that these features also interact with additional features, as opposed to completely different features interacting. This key insight enables us to drastically prune the space of potential feature interactions to those that will have a true impact at source code level. This substantial space reduction can be leveraged by analysis techniques that are based on feature interactions (e.g Combinatorial Interaction Testing). Our observation is based on eight variable systems, implemented in Java and C, totaling over nine million LoC, with over seven thousand feature interactions.
Reference:
Predicting Higher Order Structural Feature Interactions in Variable Systems (Stefan Fischer, Lukas Linsbauer, Alexander Egyed, Roberto E. Lopez-Herrejon), In Proceedings of the 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, Madrid, Spain, September 23-29, 2018, IEEE Computer Society, 2018.
Bibtex Entry:
@Conference{DBLP:conf/icsm/0006LEL18,
author = {Stefan Fischer and Lukas Linsbauer and Alexander Egyed and Roberto E. Lopez{-}Herrejon},
booktitle = {Proceedings of the 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, Madrid, Spain, September 23-29, 2018},
title = {Predicting Higher Order Structural Feature Interactions in Variable Systems},
year = {2018},
pages = {252-263},
publisher = {IEEE Computer Society},
abstract = {Robust and effective support for the detection and management of software features and their interactions is crucial for many development tasks but has proven to be an elusive goal despite extensive research on the subject. This is especially challenging for variable systems where multiple variants of a system and their features must be collectively considered. Here an important issue is the typically large number of feature interactions that can occur in variable systems. We propose a method that computes, from a set of known source code level interactions of n features, the relevant interactions involving n+1 features. Our method is based on the insight that, if a set of features interact, it is much more likely that these features also interact with additional features, as opposed to completely different features interacting. This key insight enables us to drastically prune the space of potential feature interactions to those that will have a true impact at source code level. This substantial space reduction can be leveraged by analysis techniques that are based on feature interactions (e.g Combinatorial Interaction Testing). Our observation is based on eight variable systems, implemented in Java and C, totaling over nine million LoC, with over seven thousand feature interactions.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/icsm/0006LEL18},
crossref = {DBLP:conf/icsm/2018},
doi = {10.1109/ICSME.2018.00035},
file = {:Conferences/ICSM 2018 - Predicting Higher Order Structural Featrue Interactions in Variable Systems/Predicting Higher Order Structural Feature Interactions in Variable Systems-preprint.pdf:PDF},
keywords = {Pro2Future},
timestamp = {Thu, 15 Nov 2018 14:09:43 +0100},
url = {https://doi.org/10.1109/ICSME.2018.00035},
}