Publications

@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{Mueller2020b,

title = {Graph-Based Performance Analysis at System- and Application-Level},

author = {Richard Müller and Tom Strempel},

url = {https://pi.informatik.uni-siegen.de/stt/40_3/01_Fachgruppenberichte/SSP2019/SSP2020_Mueller.pdf},

year  = {2020},

date = {2020-01-01},

booktitle = {11th Symposium on Software Performance},

address = {Leipzig, Germany},

abstract = {The Kieker plugin for jQAssistant transforms monitored log data into graphs to support software engineers with performance analysis. In this paper, we describe how we have extended and improved this plugin to support performance analysis at system- and application-level and how we have evaluated its correctness and scalability using data from recent experiments. This is a first step to replicate complete experiments in the field of performance analysis using graphs.},

keywords = {Cypher, graph database, Java, jQAssistant, Jupyter notebook, Kieker, Neo4j, performance analysis, Python},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Muller2019b,

title = {Graph-Based Analysis and Visualization of Software Traces},

author = {Richard Müller and Matteo Fischer},

url = {http://pi.informatik.uni-siegen.de/stt/39_4/01_Fachgruppenberichte/SSP2019/SSP2019_Mueller.pdf},

year  = {2019},

date = {2019-01-01},

booktitle = {10th Symposium on Software Performance},

address = {Würzburg, Germany},

abstract = {Graphs are a suitable representation of software artifacts' data created during development and maintenance activities. Software traces monitored with Kieker are one example of such data. We present a jQAssistant plugin that scans event-based Kieker traces and stores them in a Neo4j graph database. This opens up new possibilities for analyzing and visualizing these traces with respect to application performance monitoring and architecture discovery. We illustrate the feasibility and usefulness of the plugin with the Bookstore application example.},

keywords = {Cypher, graph database, Java, jQAssistant, Kieker, Neo4j, performance 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}

}

@inproceedings{Muller2018,

title = {Towards an Open Source Stack to Create a Unified Data Source for Software Analysis and Visualization},

author = {Richard Müller and Dirk Mahler and Michael Hunger and Jens Nerche and Markus Harrer},

doi = {10.1109/VISSOFT.2018.00019},

year  = {2018},

date = {2018-01-01},

booktitle = {6th IEEE Working Conference on Software Visualization},

publisher = {IEEE},

address = {Madrid, Spain},

abstract = {The beginning of every software analysis and visualization process is data acquisition. However, there are various sources of data about a software system. The methods used to extract the relevant data are as diverse as the sources are. Furthermore, integration and storage of heterogeneous data from different software artifacts to form a unified data source are very challenging. In this paper, we introduce an extensible open source stack to take the first step to solve these challenges. We show its feasibility by analyzing and visualizing JUnit and provide answers regarding the schema, selection, and implementation of software artifacts' data.},

keywords = {D3, graph database, Java, JavaScript, jQAssistant, jQAssistant dashboard, Neo4j, open source, query, React, schema, software analysis, software visualization},

pubstate = {published},

tppubtype = {inproceedings}

}