What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Data mining, Software, Artificial intelligence, Machine learning and Code.
His work carried out in the field of Data mining brings together such families of science as Cross project, Selection, Project management and Software quality assurance.
The Software development research Tim Menzies does as part of his general Software study is frequently linked to other disciplines of science, such as Variance, therefore creating a link between diverse domains of science.
His studies deal with areas such as Quality, Software quality, Task and Search-based software engineering as well as Artificial intelligence.
His research integrates issues of Software system and Training set in his study of Machine learning.
His biological study spans a wide range of topics, including Code review, Fagan inspection and Detector.
His most cited work include:
- Data Mining Static Code Attributes to Learn Defect Predictors (977 citations)
- On the relative value of cross-company and within-company data for defect prediction (416 citations)
- Defect prediction from static code features: current results, limitations, new approaches (278 citations)
What are the main themes of his work throughout his whole career to date?
Tim Menzies focuses on Software, Artificial intelligence, Machine learning, Software engineering and Data mining.
His Software research is multidisciplinary, incorporating perspectives in Estimation and Data science.
His work on Evolutionary algorithm and Range as part of general Artificial intelligence research is often related to Context, thus linking different fields of science.
His Machine learning study combines topics from a wide range of disciplines, such as Search-based software engineering and Code.
His Software engineering research incorporates elements of Quality, Software system, Requirements engineering and Software development.
His Data mining research incorporates themes from k-nearest neighbors algorithm, Project management, Cluster analysis and Pruning.
He most often published in these fields:
- Software (32.52%)
- Artificial intelligence (28.54%)
- Machine learning (22.57%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (28.54%)
- Software (32.52%)
- Machine learning (22.57%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Software, Machine learning, Software engineering and Software analytics.
His Artificial intelligence study often links to related topics such as Estimation.
In general Software study, his work on Software development, COCOMO, Source lines of code and Software quality often relates to the realm of Work, thereby connecting several areas of interest.
His work deals with themes such as Search-based software engineering, Data mining and Process, which intersect with Machine learning.
His biological study deals with issues like Software system, which deal with fields such as Sample and Set.
Tim Menzies studied Software analytics and Data science that intersect with Field.
Between 2014 and 2021, his most popular works were:
- Tuning for software analytics (140 citations)
- Heterogeneous Defect Prediction (106 citations)
- What is wrong with topic modeling? And how to fix it using search-based software engineering (96 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Tim Menzies mostly deals with Software, Artificial intelligence, Machine learning, Software analytics and Data mining.
His Software study integrates concerns from other disciplines, such as Data modeling, Algorithm, Feature selection and Data science.
He has researched Artificial intelligence in several fields, including Sampling, Mathematical optimization and Task.
His Machine learning research includes elements of Search-based software engineering and Differential evolution.
Tim Menzies interconnects Software bug, Analytics, Software engineering and Task analysis in the investigation of issues within Software analytics.
As part of one scientific family, he deals mainly with the area of Data mining, narrowing it down to issues related to the Cluster analysis, and often Estimator, Cache, Database and Obfuscation.
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