Learning a Classifier for Prediction of Maintainability based on Static Analysis Tools
Markus Schnappinger, Mohd Hafeez Osman, Alexander Pretschner, Arnaud Fietzke
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.
2019 IEEE/ACM 27th INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION (ICPC)
Operationalised 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 AN SOFTWARE TECHNOLOGY, VOLUME 62, PAGES 101-123, JUNE 2015
