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The Curse of Copy&Paste - Cloning in Requirements Specifications
Christoph Domann, Elmar Juergens, Jonathan Streit
PROCEEDINGS OF INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM), OCTOBER 2009
The Quamoco Product Quality Modeling and Assessment Approach
Stefan Wagner, Klaus Lochmann, Lars Heinemann, Michael Kläs, Adam Trendowicz, Reinhold Plösch, Andreas Seidl, Andreas Goeb, Jonathan Streit
PROCEEDINGS OF INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), ZURICH, JUNE 2012
Near Field Communication (NFC) in an Automotive Environment
Rainer Steffen, Jörg Preißinger, Tobias Schöllermann, Armin Müller, Ingo Schnabel
PROCEEDINGS OF 2ND INTERNATIONAL WORKSHOP ON NFC, MONACO, APRIL 2010
Support of performance optimizations through analysis of sampled mainframe consumption measurements.
Stefan Laner
MASTER'S THESIS, INSTITUTE OF COMPUTER SCIENCE, TECHNICAL UNIVERSITY OF MUNICH, IN COOPERATION WITH ITESTRA GMBH, 2013
Can Clone Detection Support Quality Assessments of Requirements Specifications?
Stefan Wagner, Jonathan Streit, Elmar Juergens, Florian Deissenboeck, Martin Felikas, Benjamin Hummel, Bernhard Schaetz, Christoph Domann
PROCEEDINGS OF ICSE'10, CAPE TOWN, SOUTH AFRICA, MAY 2010
Operationalized product quality models and assessment: The Quamoco approach
Stefan Wagner, Andreas Goeb, Jonathan Streit, Lars Heinemann, Michael Kläs, Klaus Lochmann, Reinhold Plösch, Andreas Seidl, Adam Trendowicz, Constanza Lampasona, Alois Mayr
INFORMATION AND SOFTWARE TECHNOLOGY, VOLUME 62, PAGES 101-123, JUNE 2015
Learning a Classifier for Prediction of Maintainability based on Static Analysis Tools
Markus Schnappinger, Mohd Hafeez Osman, Alexander Pretschner, Arnaud Fietzke
2019 IEEE/ACM 27th INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION (ICPC)
Download the comprehensive research paper on predicting software maintainability using static analysis tools. This scholarly work, conducted by experts from the Technical University of Munich and itestra GmbH, meticulously examines source code from various projects. The study employs machine learning algorithms to forecast the maintainability of software classes based on code metrics. The findings reveal promising techniques for automated maintainability assessments, providing valuable insights for developers striving to maintain high-quality software systems. Access the full document to delve into the methodologies, results, and practical implications of this pioneering research.