What is he best known for?
The fields of study he is best known for:
- Software
- Operating system
- Programming language
Harald C. Gall mainly investigates Software engineering, Data mining, Software, Software maintenance and Source code.
His work in Software engineering covers topics such as Software system which are related to areas like Software development and Configuration management.
His Data mining study combines topics from a wide range of disciplines, such as Recommender system, Static program analysis, Predictive modelling, Statistical classification and Maintainability.
In his work, Information technology, Workflow and Applications of artificial intelligence is strongly intertwined with Domain, which is a subfield of Software.
His Software maintenance research includes elements of Software evolution, Systems engineering, Object-oriented programming, Information retrieval and Taxonomy.
His Source code research includes themes of Software bug, Code, World Wide Web and Semantics, Artificial intelligence.
His most cited work include:
- Populating a Release History Database from version control and bug tracking systems (495 citations)
- Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (455 citations)
- Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction (443 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Software engineering, Software, Source code, Software development and Software system.
His research in Software engineering intersects with topics in Software maintenance, Code, Personal software process, Software quality and Workflow.
His work on Software evolution as part of general Software study is frequently connected to Empirical research, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Source code study deals with World Wide Web intersecting with Information retrieval.
His Software system study integrates concerns from other disciplines, such as Software analysis pattern, Collaborative software and Theoretical computer science.
His Data mining study combines topics in areas such as Predictive modelling, Machine learning, Software bug and Artificial intelligence.
He most often published in these fields:
- Software engineering (40.37%)
- Software (36.24%)
- Source code (23.85%)
What were the highlights of his more recent work (between 2015-2021)?
- Software (36.24%)
- Software engineering (40.37%)
- Source code (23.85%)
In recent papers he was focusing on the following fields of study:
Software, Software engineering, Source code, Empirical research and Software quality are his primary areas of study.
His Software research integrates issues from Java, Software deployment and Documentation.
His Software engineering research incorporates themes from Code, Unit testing, Test case, Commit and Cloud computing.
Harald C. Gall has researched Source code in several fields, including Program comprehension, Static analysis, World Wide Web and Cluster analysis.
His Software quality research integrates issues from Workflow and Static program analysis.
His research integrates issues of Software evolution, Software construction and Recommender system in his study of Data mining.
Between 2015 and 2021, his most popular works were:
- Software engineering for machine learning: a case study (155 citations)
- What would users change in my app? summarizing app reviews for recommending software changes (143 citations)
- Combining text mining and data mining for bug report classification (90 citations)
In his most recent research, the most cited papers focused on:
- Software
- Operating system
- Programming language
His primary areas of study are Empirical research, Software engineering, Software, World Wide Web and Software bug.
His research in Software engineering intersects with topics in Software system, Software development and Component-based software engineering.
His Software development research incorporates themes from Container and Source lines of code.
His Software research incorporates elements of Test suite, Semantics, Information retrieval and Source code.
Many of his research projects under World Wide Web are closely connected to Qualitative analysis and Natural language parsing with Qualitative analysis and Natural language parsing, tying the diverse disciplines of science together.
His Software bug research is multidisciplinary, incorporating elements of Precision and recall, Android, Maintenance engineering and Crash.
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