Veröffentlichungen

@inproceedings{Mueller2021b,

title = {Experiences in Replicating an Experiment on Comparing Static and Dynamic Coupling Metrics},

author = {Richard Müller and Dirk Mahler and Christopher Klinkmüller},

url = {http://ceur-ws.org/Vol-3043/short2.pdf},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {12th Symposium on Software Performance},

publisher = {CEUR},

address = {Leipzig, Germany},

abstract = {In software engineering, coupling metrics are used to assess the quality of a software system's architecture, especially its maintainability and understandability. On an abstract level, two types of coupling metrics can be distinguished: static metrics are solely based on source and/or byte code, and dynamic metrics also take observed run-time behavior into account. While both metric types complement each other, a recent study by Schnoor and Hasselbring suggests that these two types are correlated. This observation indicates that to a certain degree both metric types encode redundant information. In this paper, we replicate the original experiment using the same data but a different tool set. Our successful replication hence substantiates the original findings. Moreover, the summary of our experience provides valuable insights to researchers who want to ensure reproducibility and replicability of their experiments. Following open science principles, we publish all data and scripts online.},

keywords = {dynamic analysis, monitoring, Neo4j, open science, replication, software metrics, static analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}