What is he best known for?
The fields of study he is best known for:
- Software
- Software engineering
- Statistics
Tracy Hall focuses on Software, Software development process, Knowledge management, Personal software process and Process management.
His research integrates issues of Data modeling, Management science, Data mining, Artificial intelligence and Machine learning in his study of Software.
The various areas that Tracy Hall examines in his Software development process study include Empirical research, Systems engineering and Critical success factor.
His Personal software process study integrates concerns from other disciplines, such as Software quality, Software Engineering Process Group and Social software engineering.
His studies deal with areas such as Software engineering, Software walkthrough, Software peer review and Software requirements as well as Social software engineering.
His study explores the link between Process management and topics such as Requirements engineering that cross with problems in Requirements analysis.
His most cited work include:
- A Systematic Literature Review on Fault Prediction Performance in Software Engineering (685 citations)
- Motivation in Software Engineering: A systematic literature review (334 citations)
- De-motivators for software process improvement: an analysis of practitioners' views (242 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Software, Software engineering, Software development, Knowledge management and Software development process.
His work deals with themes such as Empirical research, Data mining and Engineering management, which intersect with Software.
His research in Data mining tackles topics such as Machine learning which are related to areas like Classifier.
He has included themes like Software maintenance, Open source, Project management and Software metric in his Software engineering study.
The concepts of his Knowledge management study are interwoven with issues in Focus group and Public relations.
His study in Software development process is interdisciplinary in nature, drawing from both Systems engineering and Process management.
He most often published in these fields:
- Software (33.86%)
- Software engineering (23.28%)
- Software development (17.99%)
What were the highlights of his more recent work (between 2015-2021)?
- Software (33.86%)
- Software bug (5.82%)
- Software engineering (23.28%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Software, Software bug, Software engineering, Code and Data mining.
His Software research incorporates themes from Coupling, Risk analysis, Task analysis, Process and Test set.
His Process research focuses on Engineering ethics and how it relates to Empirical research.
His Software engineering research includes themes of Object-oriented modeling, Open source and Software maintenance.
In Code, Tracy Hall works on issues like Software development, which are connected to World Wide Web.
He interconnects Predictive modelling, Machine learning, Range, Artificial intelligence and Data quality in the investigation of issues within Data mining.
Between 2015 and 2021, his most popular works were:
- Software defect prediction: do different classifiers find the same defects? (52 citations)
- Mutation-aware fault prediction (35 citations)
- Building an Ensemble for Software Defect Prediction Based on Diversity Selection (26 citations)
In his most recent research, the most cited papers focused on:
- Software
- Statistics
- Software engineering
His primary areas of investigation include Data mining, Software bug, Predictive modelling, Artificial intelligence and Machine learning.
His Data mining research is multidisciplinary, incorporating elements of Coupling, Software, Open source and Code.
Tracy Hall undertakes interdisciplinary study in the fields of Software and Explanatory power through his research.
As part of one scientific family, Tracy Hall deals mainly with the area of Software bug, narrowing it down to issues related to the Data quality, and often Software repository, Java and Defect tracking.
His Predictive modelling research is multidisciplinary, relying on both Majority rule, Classifier, Random subspace method, Range and Java code.
The study incorporates disciplines such as Software metric and Test case in addition to Artificial intelligence.
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