Publications

@inproceedings{Baum2020,

title = {Fostering Collaboration of Academia and Industry by Open Source Software},

author = {David Baum and Pascal Kovacs and Richard Müller},

doi = {10.18420/SE2020_52},

year  = {2020},

date = {2020-01-01},

booktitle = {Software Engineering: Forschung-Innovation-Transfer},

address = {Innsbruck, Austria},

abstract = {In 2017 and 2018 we released two of our research prototypes as open source. We explain our motivation and concerns at that time and compare them with our actual experience. We also describe how open source releases enabled collaboration with industrial partners. Finally, we show how research projects can extend their funding through grants for open source software. We share our experiences with the initiative Google Summer of Code and show how we overcame bureaucratical hurdles and how our research has benefited from participating in this program.},

keywords = {academia, collaboration, Getaviz, industry, jQAssistant, jQAssistant dashboard, open source},

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}

}