2023 - Research.com Computer Science in Germany Leader Award
2010 - ACM Fellow For contributions to automated debugging, and to mining software archives.
Programming language, Software, Data mining, Debugging and Delta Debugging are his primary areas of study. His Software research incorporates themes from Automatic test pattern generation and Component. The concepts of his Data mining study are interwoven with issues in Software bug, Artificial intelligence, Pattern recognition, Static analysis and Code smell.
His Artificial intelligence research integrates issues from Machine learning, Software maintenance, Set and Natural language processing. His Debugging study combines topics from a wide range of disciplines, such as Recommendation model and Software engineering. His Delta Debugging study combines topics in areas such as Tracing and Algorithmic program debugging.
The scientist’s investigation covers issues in Programming language, Software, Software engineering, Debugging and Code. His studies link Delta Debugging with Programming language. His Software research is multidisciplinary, relying on both Empirical research, Data mining and Source code.
His Data mining research includes themes of Machine learning and Artificial intelligence. Andreas Zeller studies Algorithmic program debugging, a branch of Debugging. His Java study integrates concerns from other disciplines, such as Object-oriented programming and Theoretical computer science.
Andreas Zeller mainly focuses on Programming language, Fuzz testing, Android, Parsing and Python. In Programming language, Andreas Zeller works on issues like Maintenance engineering, which are connected to Visualization. His Fuzz testing research incorporates elements of Replication, Lift, Byte and Algorithm.
The various areas that Andreas Zeller examines in his Android study include Test, Pointer, User interface, App store and Empirical research. Andreas Zeller focuses mostly in the field of Debugging, narrowing it down to topics relating to Program analysis and, in certain cases, Process and Semantics. Andreas Zeller interconnects Coding, Software engineering, Data science and Code in the investigation of issues within Software.
Andreas Zeller mainly investigates Fuzz testing, Programming language, Software, Code and Parsing. His study in Fuzz testing is interdisciplinary in nature, drawing from both Symbolic data analysis, Constraint and JavaScript. Many of his studies on Programming language apply to Robustness as well.
The Software study combines topics in areas such as Test, Control, Coding and Software engineering. His work deals with themes such as Visualization, Software quality and Maintenance engineering, which intersect with Code. His Parsing study combines topics in areas such as Lexical analysis, Security token, Byte and Lisp.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Mining version histories to guide software changes
T. Zimmermann;A. Zeller;P. Weissgerber;S. Diehl.
IEEE Transactions on Software Engineering (2005)
Simplifying and isolating failure-inducing input
A. Zeller;R. Hildebrandt.
IEEE Transactions on Software Engineering (2002)
Mining metrics to predict component failures
Nachiappan Nagappan;Thomas Ball;Andreas Zeller.
international conference on software engineering (2006)
When do changes induce fixes
Jacek Śliwerski;Thomas Zimmermann;Andreas Zeller.
mining software repositories (2005)
Predicting Defects for Eclipse
Thomas Zimmermann;Rahul Premraj;Andreas Zeller.
model driven engineering languages and systems (2007)
Locating causes of program failures
Holger Cleve;Andreas Zeller.
international conference on software engineering (2005)
Isolating cause-effect chains from computer programs
Andreas Zeller.
foundations of software engineering (2002)
Why Programs Fail: A Guide to Systematic Debugging
Andreas Zeller.
(2005)
Mining version histories to guide software changes
T. Zimmermann;P. Weibgerber;S. Diehl;A. Zeller.
international conference on software engineering (2004)
Predicting Faults from Cached History
Sunghun Kim;T. Zimmermann;E.J. Whitehead;A. Zeller.
international conference on software engineering (2007)
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