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ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Harald Gall

Registered user since Fri 25 May 2018

Name:Harald Gall
Bio:

Harald Gall is professor of Software Engineering in the Department of Informatics at the University of Zurich, Switzerland. Prior to that, he was associate professor at the Technical University of Vienna, Austria, in the Distributed Systems Group, where he also received his PhD (Dr. techn.) and master’s degree (Dipl.-Ing.) in Informatics.

He held visiting positions at Microsoft Research in Redmond, USA, and University of Washington in Seattle, USA.

His research interests are in software engineering with focus on software evolution, software architecture, software quality analysis, mining software repositories, and cloud-based software engineering.

He is probably best known for his work on software evolution analysis and mining software archives. Since 1997 he has worked on devising ways in which mining these repositories can help to better understand software development, to devise predictions about quality attributes, and to exploit this knowledge in software analysis tools such as Evolizer, ChangeDistiller, or SOFAS.

He is an Associate Editor of the Journal on Empirical Software Engineering (since 2015), IEEE Software (since 2015), and the Computing Journal (since 2011). He is a former Associate Editor of IEEE Transactions on Software Engineering (2010-14).

Country:Switzerland
Affiliation:University of Zurich
Research interests:Empirical Software Engineering, Software Quality Analysis, Mining Software Repositories, SE and AI

Contributions

ICSE 2019 Interactive Production Performance Feedback in the IDE
Software Engineering for Machine Learning: A Case Study
Committee Member in Program Committee within the ACM Student Research Competition-track
Automated Reporting of Anti-Patterns and Decay in Continuous Integration
DRONE: A Tool to Detect and Repair Directive Defects in Java APIs Documentation
When Code Completion Fails: a Case Study on Real-World Completions
MSR 2019 On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later
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