What is he best known for?
The fields of study he is best known for:
- Software
- Programming language
- Operating system
Software, Software metric, Object-oriented programming, Software development and Software engineering are his primary areas of study.
His research integrates issues of Maintainability, Data mining and Data science in his study of Software.
His Software metric research includes elements of Object-oriented design and Theoretical computer science.
The various areas that he examines in his Software development study include Quality, Engineering management, Software system, Code refactoring and Empirical research.
Within one scientific family, Steve Counsell focuses on topics pertaining to Software quality under Software engineering, and may sometimes address concerns connected to Set, Artificial intelligence, Machine learning, Data modeling and Regression analysis.
His biological study spans a wide range of topics, including Software fault tolerance and Variables.
His most cited work include:
- A Systematic Literature Review on Fault Prediction Performance in Software Engineering (685 citations)
- An evaluation of the MOOD set of object-oriented software metrics (289 citations)
- A conceptual model for the process of IT innovation adoption in organizations (216 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Software engineering, Software, Java, Code refactoring and Empirical research.
His Software engineering research is multidisciplinary, relying on both Software development and Software metric.
His work deals with themes such as Key, Data mining and Coupling, which intersect with Software.
His Data mining research incorporates themes from Slicing, Hypermedia, Cost estimate, Artificial intelligence and Machine learning.
His Code refactoring research includes themes of Software maintenance, Set and Code smell.
His Empirical research study combines topics in areas such as Cohesion, Data science and Set.
He most often published in these fields:
- Software engineering (23.02%)
- Software (21.51%)
- Java (18.87%)
What were the highlights of his more recent work (between 2016-2021)?
- Software engineering (23.02%)
- Software (21.51%)
- Empirical research (17.74%)
In recent papers he was focusing on the following fields of study:
Steve Counsell mainly focuses on Software engineering, Software, Empirical research, Code refactoring and Software system.
His study explores the link between Software engineering and topics such as Software development that cross with problems in Scope and Software deployment.
His work on Software repair as part of general Software study is frequently linked to Potential change, bridging the gap between disciplines.
The study incorporates disciplines such as Test, Class, Java, Gender diversity and Data science in addition to Empirical research.
Steve Counsell interconnects Software design pattern, Theoretical computer science, Open source software and Code smell in the investigation of issues within Code refactoring.
Steve Counsell works mostly in the field of Source code, limiting it down to concerns involving Natural language processing and, occasionally, Artificial intelligence, Domain, Software quality and Real-time computing.
Between 2016 and 2021, his most popular works were:
- The relationship between evolutionary coupling and defects in large industrial software (20 citations)
- Can you tell me if it smells?: A study on how developers discuss code smells and anti-patterns in Stack Overflow (19 citations)
- An experimental search-based approach to cohesion metric evaluation (16 citations)
In his most recent research, the most cited papers focused on:
- Software
- Programming language
- Operating system
His main research concerns Software, Artificial intelligence, Empirical research, Software engineering and Machine learning.
His study looks at the relationship between Software and topics such as Coupling, which overlap with Legacy system, Explanatory power, Regression analysis and Acceptance testing.
His Artificial intelligence research incorporates elements of Software bug and Interoperability.
His Empirical research research is multidisciplinary, incorporating elements of Prediction interval, Gender diversity, Data science and Code refactoring.
His study in Software engineering is interdisciplinary in nature, drawing from both Java code, Software development, Object-oriented modeling and Social software engineering.
His work carried out in the field of Machine learning brings together such families of science as Classifier, Sample and Statistical significance.
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