Explaining anomalies in feature models (bibtex)
by Kowal, Matthias and Ananieva, Sofia
Abstract:
The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impos-sible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Expla-nations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explain-ing different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short ex-planations expressed in a user-friendly manner and by em-phasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.
Reference:
Explaining anomalies in feature models (Kowal, Matthias and Ananieva, Sofia), In Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences - GPCE 2016, ACM, 2016.
Bibtex Entry:
@inproceedings{Kowal:2016:EAF:2993236.2993248,
abstract = {The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impos-sible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Expla-nations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explain-ing different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short ex-planations expressed in a user-friendly manner and by em-phasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.},
address = {New York, NY, USA},
author = {Kowal, Matthias and Ananieva, Sofia and Th{\"{u}}m, Thomas},
booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences - GPCE 2016},
doi = {10.1145/2993236.2993248},
isbn = {9781450344463},
issn = {03621340},
keywords = { Explanations, Feature Models, Software Product Lines,Anomalies,daps},
mendeley-tags = {daps},
pages = {132--143},
publisher = {ACM},
series = {GPCE 2016},
title = {{Explaining anomalies in feature models}},
url = {http://dl.acm.org/citation.cfm?doid=2993236.2993248},
year = {2016}
}
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