What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Operating system
His primary areas of study are Software engineering, Data mining, Object-oriented programming, Programming language and Source code.
His study in Software engineering is interdisciplinary in nature, drawing from both Software system, Software maintenance, Legacy system, Unified Modeling Language and Software architecture.
His Unified Modeling Language research includes elements of Web application, Application software, Comprehension and Web modeling.
His Web application study combines topics in areas such as Web testing and Database.
His studies deal with areas such as Aspect-oriented programming, Theoretical computer science and Java as well as Object-oriented programming.
Specifically, his work in Programming language is concerned with the study of Static program analysis.
His most cited work include:
- Analysis and testing of Web applications (432 citations)
- Evolutionary testing of classes (355 citations)
- State-Based Testing of Ajax Web Applications (209 citations)
What are the main themes of his work throughout his whole career to date?
Paolo Tonella mainly focuses on Software engineering, Programming language, Web application, Test case and Artificial intelligence.
His work focuses on many connections between Software engineering and other disciplines, such as Software, that overlap with his field of interest in Computer security.
His biological study spans a wide range of topics, including Web testing, Data mining, Database and The Internet, Web modeling.
His work deals with themes such as Algorithm, Multi-agent system and Test Management Approach, which intersect with Test case.
His work investigates the relationship between Artificial intelligence and topics such as Natural language processing that intersect with problems in Identifier.
He combines subjects such as Program comprehension, Static program analysis and Code with his study of Source code.
He most often published in these fields:
- Software engineering (31.45%)
- Programming language (16.96%)
- Web application (16.25%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (13.07%)
- Machine learning (4.95%)
- Web testing (11.66%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Web testing, Web application and Deep learning.
His Artificial intelligence study incorporates themes from State and Natural language processing.
His Machine learning study integrates concerns from other disciplines, such as Software system and Anomaly detection.
The study incorporates disciplines such as Data mining, Test data, Set and Test case in addition to Web application.
His Web development research is multidisciplinary, relying on both Web design and Software engineering.
He works mostly in the field of Software engineering, limiting it down to topics relating to Model inference and, in certain cases, Computer security, as a part of the same area of interest.
Between 2015 and 2021, his most popular works were:
- Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (106 citations)
- Robula+: an algorithm for generating robust XPath locators for web testing (38 citations)
- Approaches and Tools for Automated End-to-End Web Testing (33 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Software
- Operating system
His primary areas of study are Web testing, Empirical research, Software, Artificial intelligence and Computer security.
The study of Web service, Web development and Web modeling are components of his Web testing research.
The Web service study combines topics in areas such as Web page and Software engineering.
His study in Software is interdisciplinary in nature, drawing from both Genetic algorithm, Fitness function, Algorithm design, Sorting and Robustness.
His study explores the link between Artificial intelligence and topics such as Machine learning that cross with problems in Anomaly detection.
His Computer security research is multidisciplinary, incorporating perspectives in Encoding, Reverse engineering and Comprehension.
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