H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 30 Citations 4,046 99 World Ranking 7268 National Ranking 4

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Software
  • Machine learning

Mike Papadakis mostly deals with Reliability engineering, Mutation testing, Mutation, Empirical research and Test case. His Reliability engineering study incorporates themes from Test suite, Feature model and White-box testing, Software construction. His Mutation research includes themes of Test and Software testing.

Mike Papadakis works mostly in the field of Empirical research, limiting it down to concerns involving Set and, occasionally, Software quality, Application software and Theoretical computer science. His Test case study combines topics from a wide range of disciplines, such as Exploit, Taint checking, Machine code and Static analysis. His work carried out in the field of Java brings together such families of science as Algorithm, Compiler and Benchmark.

His most cited work include:

  • Mutation Testing Advances: An Analysis and Survey (129 citations)
  • Static analysis of android apps (128 citations)
  • Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-Wise Test Configurations for Software Product Lines (124 citations)

What are the main themes of his work throughout his whole career to date?

Mutation testing, Mutation, Artificial intelligence, Software and Machine learning are his primary areas of study. His Mutation research is multidisciplinary, relying on both Data mining, Reliability engineering, Set, Test case and Algorithm. The study incorporates disciplines such as Unit testing and White-box testing in addition to Reliability engineering.

His Test case study incorporates themes from Test data generation, Distributed computing, Selection, Software fault tolerance and System testing. His Artificial intelligence research incorporates elements of Key and Natural language processing. In the field of Software, his study on Software product line overlaps with subjects such as Scalability and Product.

He most often published in these fields:

  • Mutation testing (33.06%)
  • Mutation (27.42%)
  • Artificial intelligence (25.00%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (25.00%)
  • Machine learning (20.97%)
  • Mutation testing (33.06%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Machine learning, Mutation testing, Mutant and Software. He has included themes like Domain, Process and Orders of magnitude in his Artificial intelligence study. His study in the fields of Deep learning and Semi-supervised learning under the domain of Machine learning overlaps with other disciplines such as Context.

Mutation testing is connected with Commit, Code and Mutation in his research. His work in Code addresses subjects such as Set, which are connected to disciplines such as Test case, Algorithm and Selection. His work deals with themes such as Variety, Software engineering and Task analysis, which intersect with Software.

Between 2019 and 2021, his most popular works were:

  • Automatic testing and improvement of machine translation (14 citations)
  • Selecting fault revealing mutants (14 citations)
  • Data-driven Simulation and Optimization for Covid-19 Exit Strategies (8 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Software
  • Statistics

His primary scientific interests are in Artificial intelligence, Mutant, Machine learning, Order and Transformer. His Artificial intelligence study combines topics in areas such as Domain, Software and Task analysis. His Mutant research spans across into areas like Theoretical computer science, Symbolic execution, Tree, Benchmark and Scalability.

His work in the fields of Machine learning, such as Deep learning, intersects with other areas such as Diversity and Context. Robustness, Random forest, Overdraft, Adversarial machine learning and False positive paradox are fields of study that intersect with his Order research. His Transformer research is multidisciplinary, incorporating elements of Consistency, Machine translation, Natural language processing and Automatic testing.

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.

Top Publications

Mutation Testing Advances: An Analysis and Survey

Mike Papadakis;Marinos Kintis;Jie Zhang;Yue Jia.
Advances in Computers (2018) (In press). (2018)

192 Citations

Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-Wise Test Configurations for Software Product Lines

Christopher Henard;Mike Papadakis;Gilles Perrouin;Jacques Klein.
IEEE Transactions on Software Engineering (2014)

189 Citations

Static analysis of android apps

Li Li;Tegawend F. Bissyand;Mike Papadakis;Siegfried Rasthofer.
Information & Software Technology (2017)

184 Citations

Metallaxis-FL: mutation-based fault localization

Mike Papadakis;Yves Le Traon.
Software Testing, Verification & Reliability (2015)

183 Citations

Trivial compiler equivalence: a large scale empirical study of a simple, fast and effective equivalent mutant detection technique

Mike Papadakis;Yue Jia;Mark Harman;Yves Le Traon.
international conference on software engineering (2015)

176 Citations

Combining multi-objective search and constraint solving for configuring large software product lines

Christopher Henard;Mike Papadakis;Mark Harman;Yves Le Traon.
international conference on software engineering (2015)

168 Citations

Automatic Mutation Test Case Generation via Dynamic Symbolic Execution

Mike Papadakis;Nicos Malevris.
international symposium on software reliability engineering (2010)

147 Citations

Comparing white-box and black-box test prioritization

Christopher Henard;Mike Papadakis;Mark Harman;Yue Jia.
international conference on software engineering (2016)

135 Citations

PIT a Practical Mutation Testing Tool for Java

Henry Coles;Thomas Laurent;Christopher Henard;Mike Papadakis.
(2016)

134 Citations

Multi-objective test generation for software product lines

Christopher Henard;Mike Papadakis;Gilles Perrouin;Jacques Klein.
software product lines (2013)

129 Citations

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

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