What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Software engineering
Data mining, Software engineering, Estimation, Cost estimate and Pair programming are her primary areas of study.
Her studies in Data mining integrate themes in fields like Software metric, Stepwise regression, Regression analysis, Data set and Case-based reasoning.
The study incorporates disciplines such as Agile usability engineering and Body of knowledge in addition to Software engineering.
The various areas that she examines in her Estimation study include Statistics, Tabu search, Support vector machine and Software development effort estimation.
Her work carried out in the field of Cost estimate brings together such families of science as Empirical process, Cost estimation models, Web development and Web engineering.
Her biological study spans a wide range of topics, including Big Five personality traits, Empirical research, Software design and Openness to experience.
Her most cited work include:
- Cross versus Within-Company Cost Estimation Studies: A Systematic Review (305 citations)
- Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review (203 citations)
- A Comparative Study of Cost Estimation Models for Web Hypermedia Applications (193 citations)
What are the main themes of her work throughout her whole career to date?
Emilia Mendes mainly focuses on Data mining, Estimation, Software engineering, Context and Software.
Emilia Mendes has researched Data mining in several fields, including Machine learning, Stepwise regression, Artificial intelligence, Case-based reasoning and Web development.
Emilia Mendes combines subjects such as Knowledge management, Cost estimate, Bayesian network, Project management and Data science with her study of Estimation.
The Cost estimate study combines topics in areas such as Web application, Cost estimation models and Web engineering.
Emilia Mendes interconnects Software verification and validation, Software development, Software construction and Empirical research in the investigation of issues within Software engineering.
Her Software research incorporates themes from Predictive modelling and Set.
She most often published in these fields:
- Data mining (25.49%)
- Estimation (24.71%)
- Software engineering (23.14%)
What were the highlights of her more recent work (between 2016-2021)?
- Context (20.39%)
- Software engineering (23.14%)
- Software (21.57%)
In recent papers she was focusing on the following fields of study:
Context, Software engineering, Software, Systematic review and Value are her primary areas of study.
Her research in Software engineering intersects with topics in Empirical research and Decision support system.
Her Software study incorporates themes from Agile software development, Replication, Project management and Process management.
As a part of the same scientific family, Emilia Mendes mostly works in the field of Field, focusing on Data science and, on occasion, Web application.
Visualization is a subfield of Data mining that Emilia Mendes investigates.
Her work is dedicated to discovering how Data mining, Training set are connected with Machine learning and other disciplines.
Between 2016 and 2021, her most popular works were:
- Taxonomies in software engineering (50 citations)
- Machine learning and microsimulation techniques on the prognosis of dementia : A systematic literature review (23 citations)
- Investigating the use of moving windows to improve software effort prediction: a replicated study (17 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Software engineering
Her main research concerns Context, Software engineering, Systematic review, Software and Systematic mapping.
Her study of Context brings together topics like Quality and Software development.
Her Software development research integrates issues from Management science, Service, Personality, Personality psychology and Set.
Many of her research projects under Software engineering are closely connected to Mechanism with Mechanism, tying the diverse disciplines of science together.
She has included themes like Machine learning, Bayesian network, Data mining and Training set in her Software study.
Her Bayesian network research incorporates elements of Critical success factor, Estimation, Project management, Product and Operations research.
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