Mark Harman

34.4k total citations · 14 hit papers
469 papers, 22.0k citations indexed

About

Mark Harman is a scholar working on Software, Information Systems and Artificial Intelligence. According to data from OpenAlex, Mark Harman has authored 469 papers receiving a total of 22.0k indexed citations (citations by other indexed papers that have themselves been cited), including 334 papers in Software, 303 papers in Information Systems and 68 papers in Artificial Intelligence. Recurrent topics in Mark Harman's work include Software Testing and Debugging Techniques (292 papers), Software Engineering Research (283 papers) and Software Reliability and Analysis Research (239 papers). Mark Harman is often cited by papers focused on Software Testing and Debugging Techniques (292 papers), Software Engineering Research (283 papers) and Software Reliability and Analysis Research (239 papers). Mark Harman collaborates with scholars based in United Kingdom, United States and Italy. Mark Harman's co-authors include Yue Jia, Shin Yoo, Robert M. Hierons, Phil McMinn, David Binkley, Federica Sarro, B.F. Jones, William B. Langdon, Yuanyuan Zhang and Jie M. Zhang and has published in prestigious journals such as Gastroenterology, Communications of the ACM and ACM Computing Surveys.

In The Last Decade

Mark Harman

454 papers receiving 20.8k citations

Hit Papers

An Analysis and Survey of the Development of Mutation T... 2001 2026 2009 2017 2010 2012 2014 2001 2007 250 500 750 1000

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mark Harman United Kingdom 71 16.0k 14.4k 4.6k 3.6k 2.6k 469 22.0k
Lionel Briand Luxembourg 71 12.9k 0.8× 14.7k 1.0× 5.5k 1.2× 3.9k 1.1× 1.1k 0.4× 415 19.2k
David Lo Singapore 73 8.5k 0.5× 15.8k 1.1× 5.4k 1.2× 4.9k 1.4× 3.4k 1.3× 599 19.3k
Grady Booch United States 28 4.6k 0.3× 7.5k 0.5× 6.9k 1.5× 3.1k 0.9× 409 0.2× 124 14.6k
John Vlissides United States 20 3.5k 0.2× 7.8k 0.5× 7.9k 1.7× 4.5k 1.3× 553 0.2× 33 14.9k
James Rumbaugh United States 21 4.1k 0.3× 6.4k 0.4× 6.0k 1.3× 2.8k 0.8× 523 0.2× 57 12.5k
Victor R. Basili United States 58 7.6k 0.5× 14.1k 1.0× 4.8k 1.0× 2.9k 0.8× 405 0.2× 342 16.8k
Ivar Jacobson United States 26 4.4k 0.3× 7.6k 0.5× 6.4k 1.4× 2.4k 0.7× 315 0.1× 82 13.0k
Erich Gamma Switzerland 12 3.0k 0.2× 6.9k 0.5× 7.1k 1.5× 4.0k 1.1× 495 0.2× 21 13.3k
Tsong Yueh Chen Australia 48 6.8k 0.4× 3.9k 0.3× 1.5k 0.3× 1.7k 0.5× 918 0.4× 324 8.3k
Somesh Jha United States 50 1.8k 0.1× 3.1k 0.2× 9.2k 2.0× 4.2k 1.2× 4.6k 1.8× 185 13.7k

Countries citing papers authored by Mark Harman

Since Specialization
Citations

This map shows the geographic impact of Mark Harman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark Harman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Harman more than expected).

Fields of papers citing papers by Mark Harman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Harman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Harman. The network helps show where Mark Harman may publish in the future.

Co-authorship network of co-authors of Mark Harman

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Harman. A scholar is included among the top collaborators of Mark Harman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark Harman. Mark Harman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Alshahwan, Nadia, et al.. (2024). Automated Unit Test Improvement using Large Language Models at Meta. 185–196. 40 indexed citations
2.
Alshahwan, Nadia, et al.. (2024). Observation-Based Unit Test Generation at Meta. 173–184. 3 indexed citations
3.
Tuli, Shreshth, et al.. (2023). Simulation-Driven Automated End-to-End Test and Oracle Inference. 122–133. 1 indexed citations
4.
Hort, Max, Zhenpeng Chen, Jie M. Zhang, Mark Harman, & Federica Sarro. (2023). Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey. 1(2). 1–52. 80 indexed citations
5.
Sarro, Federica, et al.. (2021). Multi-Objective Software Effort Estimation: A Replication Study. IEEE Transactions on Software Engineering. 48(8). 3185–3205. 27 indexed citations
6.
Sarro, Federica, et al.. (2019). The Assessor's Dilemma: Improving Bug Repair via Empirical Game Theory. IEEE Transactions on Software Engineering. 47(10). 2143–2161. 10 indexed citations
7.
Papadakis, Mike, Yue Jia, Mark Harman, & Yves Le Traon. (2015). Trivial compiler equivalence: a large scale empirical study of a simple, fast and effective equivalent mutant detection technique. International Conference on Software Engineering. 1. 936–946. 110 indexed citations
8.
Paixão, Matheus, et al.. (2015). Improving the Module Clustering of a C/C++ Editor using a Multi-objective Genetic Algorithm. 1 indexed citations
9.
Henard, Christopher, Mike Papadakis, Mark Harman, & Yves Le Traon. (2015). Combining multi-objective search and constraint solving for configuring large software product lines. International Conference on Software Engineering. 1. 517–528. 84 indexed citations
10.
Finkelstein, Anthony, et al.. (2014). App Store Analysis: Mining App Stores for Relationships between Customer, Business and Technical Characteristics. 25 indexed citations
11.
Tonella, Paolo, Cu Nguyen, Alessandro Marchetto, Kiran Lakhotia, & Mark Harman. (2013). Automated generation of state abstraction functions using data invariant inference. 75–81. 5 indexed citations
12.
Paige, Richard F., Mark Harman, & James R. Williams. (2013). Proceedings of the 1st International Workshop on Combining Modelling and Search-Based Software Engineering. International Conference on Software Engineering. 4 indexed citations
13.
Binkley, David, Mark Harman, & Jens Krinke. (2008). Animated Visualisation of Static Analysis: Characterising, Explaining and Exploiting the Approximate Nature of Static Analysis. 1 indexed citations
14.
Harman, Mark. (2007). Automated Test Data Generation using Search Based Software Engineering. International Conference on Software Engineering. 199. 3 indexed citations
15.
Harman, Mark & John A. Clark. (2004). Metrics are fitness functions too. 58–69. 68 indexed citations
16.
Guo, Qiang, et al.. (2004). Computing unique input/output sequences using genetic algorithms. 1 indexed citations
17.
Cantú‐Paz, Erick, Kalyanmoy Deb, Larry Davis, et al.. (2003). Genetic and evolutionary computation - GECCO 2003 : Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 : proceedings. Springer eBooks. 1 indexed citations
18.
Rolleston, J. D., Walter H. Sokel, Judith Ryan, et al.. (2002). A Companion to the Works of Franz Kafka.
19.
Harman, Mark, et al.. (1999). Towards a Maturity Model for Empirical Studies of Software Testing. 1 indexed citations
20.
Danicic, Sebastian & Mark Harman. (1997). Program Slicing using Functional Networks(Concurrency Theory and Applications '96). Kyoto University Research Information Repository (Kyoto University). 996. 54–65. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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