Exploiting Traceability Uncertainty Between Software Architectural Models and Performance Analysis Results (bibtex)
by Catia Trubiani, Achraf Ghabi, Alexander Egyed
Abstract:
While software architecture performance analysis is a wellstudied field, it is less understood how the analysis results (i.e., mean values, variances, and/or probability distributions) trace back to the architectural model elements (i.e., software components, interactions among components, deployment nodes). Yet, understanding this traceability is critical for understanding the analysis result in context of the architecture. The goal of this paper is to automate the traceability between software architectural models and performance analysis results by investigating the uncertainty while bridging these two domains. Our approach makes use of performance antipatterns to deduce the logical consequences between the architectural elements and analysis results and automatically build a graph of traces to identify the most critical causes of performance flaws. We developed a tool that jointly considers SOftware and PErformance concepts (SoPeTraceAnalyzer), and it automatically builds model-to-results traceability links. The benefit of the tool is illustrated by means of a case study in the e-health domain.
Reference:
Exploiting Traceability Uncertainty Between Software Architectural Models and Performance Analysis Results (Catia Trubiani, Achraf Ghabi, Alexander Egyed), In Proceedings of the Software Architecture - 9th European Conference, (ECSA 2015), Dubrovnik/Cavtat, Croatia, 2015.
Bibtex Entry:
@Conference{DBLP:conf/ecsa/TrubianiGE15,
  author    = {Catia Trubiani and Achraf Ghabi and Alexander Egyed},
  title     = {Exploiting Traceability Uncertainty Between Software Architectural Models and Performance Analysis Results},
  booktitle = {Proceedings of the Software Architecture - 9th European Conference, (ECSA 2015), Dubrovnik/Cavtat, Croatia},
  year      = {2015},
  pages     = {305--321},
  abstract  = {While software architecture performance analysis is a wellstudied
	field, it is less understood how the analysis results (i.e., mean
	values, variances, and/or probability distributions) trace back to
	the architectural model elements (i.e., software components, interactions
	among components, deployment nodes). Yet, understanding this traceability
	is critical for understanding the analysis result in context of the
	architecture. The goal of this paper is to automate the traceability
	between software architectural models and performance analysis results
	by investigating the uncertainty while bridging these two domains.
	Our approach makes use of performance antipatterns to deduce the
	logical consequences between the architectural elements and analysis
	results and automatically build a graph of traces to identify the
	most critical causes of performance flaws. We developed a tool that
	jointly considers SOftware and PErformance concepts (SoPeTraceAnalyzer),
	and it automatically builds model-to-results traceability links.
	The benefit of the tool is illustrated by means of a case study in
	the e-health domain.},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/ecsa/TrubianiGE15},
  crossref  = {DBLP:conf/ecsa/2015},
  doi       = {10.1007/978-3-319-23727-5_26},
  file      = {:Conferences\\ECSA 2015 - Exploiting Traceability Uncertainty between Models and Performance Analysis\\Exploiting Traceability Uncertainty between Models and Performance Analysis-preprint.pdf:PDF},
  keywords  = {},
  timestamp = {Fri, 04 Sep 2015 13:14:36 +0200},
  url       = {http://dx.doi.org/10.1007/978-3-319-23727-5_26},
}
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