PUMA publications for /user/snowball/reverse_engineeringhttps://puma.ub.uni-stuttgart.de/user/snowball/reverse_engineeringPUMA RSS feed for /user/snowball/reverse_engineering2024-03-28T11:08:17+01:00An Industrial Case Study on Quality Impact Prediction for Evolving Service-Oriented Softwarehttps://puma.ub.uni-stuttgart.de/bibtex/29e69311e27a17d9204b2aac959c9a5e9/snowballsnowball2017-08-08T09:12:33+02:00case_study dtmc industrial_software lqn palladio performance_prediction reliability_prediction reverse_engineering service-oriented_software trade-off_analysis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heiko Koziolek" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/0"><span itemprop="name">H. Koziolek</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bastian Schlich" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/1"><span itemprop="name">B. Schlich</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Carlos Bilich" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/2"><span itemprop="name">C. Bilich</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Roland Weiss" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/3"><span itemprop="name">R. Weiss</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Becker" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/4"><span itemprop="name">S. Becker</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Klaus Krogmann" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/5"><span itemprop="name">K. Krogmann</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mircea Trifu" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/6"><span itemprop="name">M. Trifu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raffaela Mirandola" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/7"><span itemprop="name">R. Mirandola</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anne Koziolek" itemprop="url" href="/person/167cf27e784a23935c632c5e2c7044aed/author/8"><span itemprop="name">A. Koziolek</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceeding of the 33rd international conference on Software engineering (ICSE 2011), Software Engineering in Practice Track</span>, </em></span><em>page <span itemprop="pagination">776--785</span>. </em><em><span itemprop="publisher">ACM, New York, NY, USA</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)<em>Acceptance Rate: 18\% (18/100).</em></span>Tue Aug 08 09:12:33 CEST 2017Proceeding of the 33rd international conference on Software engineering (ICSE 2011), Software Engineering in Practice TrackAcceptance Rate: 18\% (18/100)776--785An Industrial Case Study on Quality Impact Prediction for Evolving Service-Oriented Software2011case_study dtmc industrial_software lqn palladio performance_prediction reliability_prediction reverse_engineering service-oriented_software trade-off_analysis Systematic decision support for architectural design decisions is a major concern for software architects of evolving service-oriented systems. In practice, architects often analyse the expected performance and reliability of design alternatives based on prototypes or former experience. Modeldriven prediction methods claim to uncover the tradeoffs between different alternatives quantitatively while being more cost-effective and less error-prone. However, they often suffer from weak tool support and focus on single quality attributes. Furthermore, there is limited evidence on their effectiveness based on documented industrial case studies. Thus, we have applied a novel, model-driven prediction method called Q-ImPrESS on a large-scale process control system consisting of several million lines of code from the automation domain to evaluate its evolution scenarios. This paper reports our experiences with the method and lessons learned. Benefits of Q-ImPrESS are the good architectural decision support and comprehensive tool framework, while one drawback is the time-consuming data collection.