What is she best known for?
The fields of study she is best known for:
- Software
- Programming language
- Operating system
Gabriele Bavota mainly investigates Software, Empirical research, Code smell, Source code and Code refactoring.
Her studies link Heuristics with Software.
Her research in Code smell intersects with topics in Code and Computer security, Code.
Her Source code study integrates concerns from other disciplines, such as Software system, Software maintenance, Software engineering and Rank.
Her study in Software engineering is interdisciplinary in nature, drawing from both Ranking and Documentation.
Gabriele Bavota has researched Code refactoring in several fields, including Cohesion and Data mining.
Her most cited work include:
- API change and fault proneness: a threat to the success of Android apps (213 citations)
- Mining StackOverflow to turn the IDE into a self-confident programming prompter (159 citations)
- Mining energy-greedy API usage patterns in Android apps: an empirical study (155 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Software engineering, Software, Source code, Empirical research and Code refactoring.
Her Software engineering research integrates issues from Software system, Software quality, Software maintenance, Software development and Software evolution.
Her biological study spans a wide range of topics, including Java, World Wide Web, Data mining and Documentation.
Her study in the field of Recommender system is also linked to topics like Mobile telephony.
The various areas that Gabriele Bavota examines in her Source code study include Class, Deep learning, Artificial intelligence and Code.
Her studies in Code refactoring integrate themes in fields like Extract class, Field, Identifier, Cohesion and Commit.
She most often published in these fields:
- Software engineering (41.76%)
- Software (37.65%)
- Source code (28.82%)
What were the highlights of her more recent work (between 2019-2021)?
- Software engineering (41.76%)
- Code (20.59%)
- Software (37.65%)
In recent papers she was focusing on the following fields of study:
Gabriele Bavota mainly focuses on Software engineering, Code, Software, Task and Artificial intelligence.
Her research integrates issues of Android, Code review, Software quality and Code refactoring in her study of Software engineering.
Her Android research includes elements of Exploit and World Wide Web.
The concepts of her Code study are interwoven with issues in Software metric, Natural language, Transformer and Source code.
Gabriele Bavota performs integrative study on Software and Empirical research.
Her work on Deep learning as part of general Artificial intelligence study is frequently linked to For loop, bridging the gap between disciplines.
Between 2019 and 2021, her most popular works were:
- Taxonomy of real faults in deep learning systems (28 citations)
- Automatically Assessing Code Understandability (10 citations)
- On learning meaningful assert statements for unit test cases (6 citations)
In her most recent research, the most cited papers focused on:
- Software
- Operating system
- Programming language
Her primary scientific interests are in Software, Software engineering, Source code, Code and Data science.
Her work on Software development process as part of general Software research is frequently linked to Empirical research, thereby connecting diverse disciplines of science.
The Software engineering study combines topics in areas such as Duplicate code and Code refactoring.
Her Source code research is multidisciplinary, incorporating elements of Program comprehension, Android, Java, Task analysis and World Wide Web.
Gabriele Bavota interconnects Software bug, Natural language, Software metric and Task in the investigation of issues within Code.
Her work carried out in the field of Data science brings together such families of science as Knowledge transfer, Mining software repositories, Code review and Open source.
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