M
Mark Harman
Researcher at University College London
Publications - 521
Citations - 33091
Mark Harman is an academic researcher from University College London. The author has contributed to research in topics: Search-based software engineering & Program slicing. The author has an hindex of 83, co-authored 506 publications receiving 29118 citations. Previous affiliations of Mark Harman include University of London & KAIST.
Papers
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Journal ArticleDOI
An Analysis and Survey of the Development of Mutation Testing
Yue Jia,Mark Harman +1 more
TL;DR: These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation testing itself is the subject of increasing interest.
Journal ArticleDOI
Regression testing minimization, selection and prioritization: a survey
Shin Yoo,Mark Harman +1 more
TL;DR: This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research.
BookDOI
Genetic and Evolutionary Computation -- GECCO-2003
Erick Cantú-Paz,James A. Foster,Kalyanmoy Deb,Lawrence Davis,Rajkumar Roy,Una-May O'Reilly,Hans-Georg Beyer,Russell Standish,Graham Kendall,Stewart W. Wilson,Mark Harman,Joachim Wegener,Dipankar Dasgupta,Mitch A. Potter,Alan C. Schultz,Kathryn A. Dowsland,Natasha Jonoska,Julian F. Miller +17 more
TL;DR: This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity, and suggests that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.
Journal ArticleDOI
The Oracle Problem in Software Testing: A Survey
TL;DR: This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice.
Journal ArticleDOI
Search-based software engineering
Mark Harman,Bryan F. Jones +1 more
TL;DR: It is argued that software engineering is ideal for the application of metaheuristic search techniques, such as genetic algorithms, simulated annealing and tabu search, which could provide solutions to the difficult problems of balancing competing competing constraints.