@inproceedings{Muller2019b,
title = {A Graph-Based Feature Location Approach Using Set Theory},
author = {Richard Müller and Ulrich Eisenecker},
doi = {10.1145/3336294.3342358},
isbn = {978-1-4503-7138-4},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {23rd Systems and Software Product Line Conference},
pages = {161--165},
publisher = {ACM},
address = {Paris, France},
series = {SPLC '19},
abstract = {The ArgoUML SPL benchmark addresses feature location in Software Product Lines (SPLs), where single features as well as feature combinations and feature negations have to be identified. We present a solution for this challenge using a graph-based approach and set theory. The results are promising. Set theory allows to exactly define which parts of feature locations can be computed and which precision and which recall can be achieved. This has to be complemented by a reliable identification of feature-dependent class and method traces as well as refinements. The application of our solution to one scenario of the benchmark supports this claim.},
keywords = {ArgoUML, benchmark, Cypher, extractive software product line adoption, feature location, graph database, Java, jQAssistant, Neo4j, reverse engineering, set theory, software product line, static analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
