Software Quality: The Complexity and Challenges of Software Engineering and Software Quality in the Cloud

This book constitutes the refereed proceedings of the 11th Software Quality Days Conference, SWQD 2019, held in Vienna, Austria, in January 2019.

The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference on software quality in Europe with a strong community. The program of the SWQD conference is designed to encompass a stimulating mixture of practical presentations and new research topics in scientific presentations. The guiding conference topic of the SWQD 2019 is The Complexity and Challenges of Software Engineering and Software Quality in the Cloud.

The 5 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 17 submissions. The volume also contains 2 invited talks. The contributions were organized in topical sections named: multi-disciplinary systems and software engineering; software quality and process improvement; software testing; knowledge engineering and machine learning; source code analysis; and software maintenance.

Contenu
Multi-Disciplinary Systems and Software Engineering.- Multi-Disciplinary Engineering of Production Systems Challenges for Quality of Control Software.- Towards a Flexible and Secure Round-Trip-Engineering Process for Production Systems Engineering with Agile Practices.- Software Quality and Process Improvement.- Relating Verification and Validation Methods to Software Product Quality Characteristics: Results of an Expert Survey.- Listen to Your Users Quality Improvement of Mobile Apps through Lightweight Feedback Analyses.- Agile Software Process Improvement by Learning from Financial and Fintech Companies: LHV Bank Case Study.- Software Testing.- Why Software Testing Fails: Common Pitfalls Observed in a Critical Smart Metering Project.- Knowledge Engineering and Machine Learning.- Mixed Reality Applications in Industry: Challenges and Research Areas.- Improving Defect Localization by Classifying the Affected Asset using Machine Learning.- Source Code Analysis.- Benefits and Drawbacks of Representing and Analyzing Source Code and Software Engineering Artifacts with Graph Databases.- Software Maintenance.- Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects.