Insights: Industry news and trends

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.

Why Software Quality Improvement Fails (and How to Succeed Nevertheless)

Jonathan Streit, Markus Pizka

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), HONOLULU, MAY 2011

https://itestra.com/wp-content/uploads/2018/03/11_itestra_why_software_quality_improvement_fails.pdf

Improve requirements documents

How is digitalization compatible with existing (legacy) software?

What is actually good software?

itestra: Success through innovation and continuity

Damage assessment by satellite and AI

Machine learning for software quality

How is digitalization compatible with existing (legacy) software?

Machine learning for software analysis

An activity-based quality model for maintainability

Replacement of a legacy system using custom transpilation

Benchmarking ongoing development output in real-life software projects

Architecture patterns in legacy systems

Meaningful names – a decisive factor in software systems

Publications