D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 41 Citations 5,899 162 World Ranking 4224 National Ranking 26

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Cross versus Within-Company Cost Estimation Studies: A Systematic Review

B.A. Kitchenham;E. Mendes;G.H. Travassos.
IEEE Transactions on Software Engineering (2007)

410 Citations

Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review

N. Salleh;E. Mendes;J. Grundy.
IEEE Transactions on Software Engineering (2011)

341 Citations

A systematic review of software maintainability prediction and metrics

Mehwish Riaz;Emilia Mendes;Ewan Tempero.
empirical software engineering and measurement (2009)

285 Citations

A Comparative Study of Cost Estimation Models for Web Hypermedia Applications

Emilia Mendes;Ian Watson;Chris Triggs;Nile Mosley.
Empirical Software Engineering (2003)

258 Citations

Effort estimation in agile software development: a systematic literature review

Muhammad Usman;Emilia Mendes;Francila Weidt;Ricardo Britto.
predictive models in software engineering (2014)

189 Citations

Web metrics - estimating design and authoring effort

E. Mendes;N. Mosley;S. Counsell.
IEEE MultiMedia (2001)

169 Citations

Why comparative effort prediction studies may be invalid

Barbara Kitchenham;Emilia Mendes.
model driven engineering languages and systems (2009)

164 Citations

Further comparison of cross-company and within-company effort estimation models for Web applications

E. Mendes;B. Kitchenham.
ieee international software metrics symposium (2004)

163 Citations

Web Engineering

Nile Mosley;Emilia Mendes.
(2006)

160 Citations

Software productivity measurement using multiple size measures

B. Kitchenham;E. Mendes.
IEEE Transactions on Software Engineering (2004)

156 Citations

Best Scientists Citing Emilia Mendes

Tim Menzies

Tim Menzies

North Carolina State University

Publications: 61

Burak Turhan

Burak Turhan

University of Oulu

Publications: 26

Alain Abran

Alain Abran

École de Technologie Supérieure

Publications: 26

Lefteris Angelis

Lefteris Angelis

Aristotle University of Thessaloniki

Publications: 21

Barbara Kitchenham

Barbara Kitchenham

Keele University

Publications: 20

Pearl Brereton

Pearl Brereton

Keele University

Publications: 19

Martin Shepperd

Martin Shepperd

Brunel University London

Publications: 19

Barry Boehm

Barry Boehm

University of Southern California

Publications: 17

Kenichi Matsumoto

Kenichi Matsumoto

Nara Institute of Science and Technology

Publications: 17

Guilherme Horta Travassos

Guilherme Horta Travassos

Federal University of Rio de Janeiro

Publications: 16

Xavier Franch

Xavier Franch

Universitat Politècnica de Catalunya

Publications: 14

Stefan Wagner

Stefan Wagner

University of Stuttgart

Publications: 13

Robert Feldt

Robert Feldt

Chalmers University of Technology

Publications: 12

Xin Yao

Xin Yao

Southern University of Science and Technology

Publications: 12

Mika V. Mäntylä

Mika V. Mäntylä

University of Oulu

Publications: 12

Kai Petersen

Kai Petersen

Blekinge Institute of Technology

Publications: 12

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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