What is she best known for?
The fields of study she is best known for:
- Programming language
- Software
- Artificial intelligence
Michele Lanza focuses on Software engineering, Software system, Software, Software development and Software evolution.
Michele Lanza interconnects Business process reengineering, Context, Personalization, Source code and Extensibility in the investigation of issues within Software engineering.
Her Software system research integrates issues from Visualization, Reverse engineering and Code.
Her Software research is multidisciplinary, incorporating perspectives in Object-oriented programming and Identification.
As a part of the same scientific study, Michele Lanza usually deals with the Software development, concentrating on World Wide Web and frequently concerns with Leverage, Teamwork and Workflow.
Her work in Software evolution addresses subjects such as Data science, which are connected to disciplines such as Software versioning and Java.
Her most cited work include:
- Object-Oriented Metrics in Practice (416 citations)
- An extensive comparison of bug prediction approaches (392 citations)
- Evaluating defect prediction approaches: a benchmark and an extensive comparison (350 citations)
What are the main themes of her work throughout her whole career to date?
Michele Lanza mainly investigates Software engineering, Software system, Software, Software development and Source code.
Her Software engineering research is multidisciplinary, incorporating elements of Context, Programming language, Software evolution, Code and Software quality.
Her Software system research is multidisciplinary, relying on both Visualization and Reverse engineering.
The Software study combines topics in areas such as Object-oriented programming, World Wide Web and Data science.
Her Source code study combines topics from a wide range of disciplines, such as Java, User interface, Theoretical computer science and Information retrieval.
Her Social software engineering research includes themes of Software peer review and Software Engineering Process Group.
She most often published in these fields:
- Software engineering (48.05%)
- Software system (35.94%)
- Software (33.59%)
What were the highlights of her more recent work (between 2013-2021)?
- Software engineering (48.05%)
- Software (33.59%)
- Source code (20.70%)
In recent papers she was focusing on the following fields of study:
Michele Lanza mainly investigates Software engineering, Software, Source code, Software development and World Wide Web.
The study incorporates disciplines such as Exploit, Context, Software system and Software analytics in addition to Software engineering.
Her research in Software system tackles topics such as Visualization which are related to areas like Plug-in.
She works mostly in the field of Software, limiting it down to topics relating to Task and, in certain cases, Technical debt.
The concepts of her Source code study are interwoven with issues in User interface, Human–computer interaction, Code refactoring, Code and Java.
Her World Wide Web research is multidisciplinary, incorporating perspectives in Program comprehension, Resource and Documentation.
Between 2013 and 2021, her most popular works were:
- Mining StackOverflow to turn the IDE into a self-confident programming prompter (159 citations)
- Sentiment analysis for software engineering: how far can we go? (76 citations)
- I know what you did last summer: an investigation of how developers spend their time (68 citations)
In her most recent research, the most cited papers focused on:
- Programming language
- Software
- Artificial intelligence
Her scientific interests lie mostly in World Wide Web, Software, Software development, Software engineering and Recommender system.
She interconnects Java and Source code in the investigation of issues within Software.
Her Software engineering study combines topics in areas such as Personal software process, Software peer review, Software Engineering Process Group and Social software engineering.
Michele Lanza combines subjects such as Context, Multimedia and Documentation with her study of Recommender system.
Her Taxonomy study incorporates themes from Software system and Data science.
The various areas that she examines in her Software system study include Visualization, Data visualization and Human–computer interaction.
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