Hakjoo Oh

1.9k total citations · 1 hit paper
63 papers, 1.1k citations indexed

About

Hakjoo Oh is a scholar working on Software, Artificial Intelligence and Information Systems. According to data from OpenAlex, Hakjoo Oh has authored 63 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Software, 36 papers in Artificial Intelligence and 31 papers in Information Systems. Recurrent topics in Hakjoo Oh's work include Software Testing and Debugging Techniques (44 papers), Software Engineering Research (28 papers) and Advanced Malware Detection Techniques (14 papers). Hakjoo Oh is often cited by papers focused on Software Testing and Debugging Techniques (44 papers), Software Engineering Research (28 papers) and Advanced Malware Detection Techniques (14 papers). Hakjoo Oh collaborates with scholars based in South Korea, United Kingdom and United States. Hakjoo Oh's co-authors include Heejo Lee, Kwangkeun Yi, Seulbae Kim, Seunghoon Woo, Kihong Heo, Hongseok Yang, Wonchan Lee, Myungho Lee, Woosuk Lee and Jisu Park and has published in prestigious journals such as IEEE Transactions on Software Engineering, The Analyst and Information and Software Technology.

In The Last Decade

Hakjoo Oh

57 papers receiving 1.1k citations

Hit Papers

VUDDY: A Scalable Approach for Vulnerable Code Clone Disc... 2017 2026 2020 2023 2017 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery
    2017 226 citations Seulbae Kim, Seunghoon Woo et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hakjoo Oh South Korea 18 694 669 479 434 141 63 1.1k
Coen De Roover Belgium 15 652 0.9× 404 0.6× 211 0.4× 337 0.8× 299 2.1× 121 878
Maverick Woo United States 11 413 0.6× 471 0.7× 652 1.4× 361 0.8× 200 1.4× 14 919
Rohit Gheyi Brazil 21 1.2k 1.8× 873 1.3× 130 0.3× 647 1.5× 318 2.3× 102 1.4k
Fan Long Canada 19 1.2k 1.7× 1.2k 1.8× 406 0.8× 424 1.0× 402 2.9× 44 1.8k
Miltiadis Allamanis United Kingdom 15 897 1.3× 424 0.6× 226 0.5× 494 1.1× 180 1.3× 31 1.1k
Gregor Snelting Germany 21 970 1.4× 502 0.8× 338 0.7× 891 2.1× 368 2.6× 64 1.4k
Wei You China 16 540 0.8× 249 0.4× 377 0.8× 367 0.8× 463 3.3× 53 1.1k
Suvda Myagmar United States 6 645 0.9× 484 0.7× 305 0.6× 131 0.3× 182 1.3× 7 754
Ira D. Baxter United States 15 1.3k 1.8× 871 1.3× 453 0.9× 490 1.1× 252 1.8× 33 1.4k
Linhai Song United States 17 655 0.9× 586 0.9× 188 0.4× 240 0.6× 726 5.1× 34 1.2k

Countries citing papers authored by Hakjoo Oh

Since Specialization
Citations

This map shows the geographic impact of Hakjoo Oh'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 Hakjoo Oh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hakjoo Oh more than expected).

Fields of papers citing papers by Hakjoo Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hakjoo Oh. 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 Hakjoo Oh. The network helps show where Hakjoo Oh may publish in the future.

Co-authorship network of co-authors of Hakjoo Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Hakjoo Oh. A scholar is included among the top collaborators of Hakjoo Oh 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 Hakjoo Oh. Hakjoo Oh 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.
Jhi, Yoon‐Chan, et al.. (2024). Effective Unit Test Generation for Java Null Pointer Exceptions. 1044–1056.
2.
Oh, Hakjoo, et al.. (2024). Quantum Probabilistic Model Checking for Time-Bounded Properties. Proceedings of the ACM on Programming Languages. 8(OOPSLA2). 557–587. 1 indexed citations
3.
Lee, Woosuk, et al.. (2023). Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Time-bounded Exhaustive Search. ACM Transactions on Programming Languages and Systems. 45(3). 1–37. 1 indexed citations
4.
Lee, Myungho, et al.. (2023). Learning Seed-Adaptive Mutation Strategies for Greybox Fuzzing. 384–396. 8 indexed citations
5.
Lee, Junhee, et al.. (2021). Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics. IEEE Transactions on Software Engineering. 48(9). 3640–3663. 5 indexed citations
6.
Oh, Hakjoo, et al.. (2019). Concolic testing with adaptively changing search heuristics. 235–245. 15 indexed citations
7.
Oh, Hakjoo, et al.. (2018). A scalable learning algorithm for data-driven program analysis. Information and Software Technology. 104. 1–13. 3 indexed citations
8.
Oh, Hakjoo, et al.. (2018). Precise and scalable points-to analysis via data-driven context tunneling. Proceedings of the ACM on Programming Languages. 2(OOPSLA). 1–29. 25 indexed citations
9.
Kim, Seulbae, Seunghoon Woo, Heejo Lee, & Hakjoo Oh. (2017). VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery. 595–614. 226 indexed citations breakdown →
10.
Oh, Hakjoo, et al.. (2017). Synthesizing imperative programs for introductory programming assignments. arXiv (Cornell University). 1 indexed citations
11.
Heo, Kihong, Hakjoo Oh, & Hongseok Yang. (2017). Learning analysis strategies for octagon and context sensitivity from labeled data generated by static analyses. Formal Methods in System Design. 53(2). 189–220. 2 indexed citations
12.
Heo, Kihong, Hakjoo Oh, & Kwangkeun Yi. (2017). Machine-Learning-Guided Selectively Unsound Static Analysis. 519–529. 39 indexed citations
13.
Heo, Kihong, Hakjoo Oh, & Kwangkeun Yi. (2017). Selective conjunction of context‐sensitivity and octagon domain toward scalable and precise global static analysis. Software Practice and Experience. 47(11). 1677–1705. 4 indexed citations
14.
Cha, Sungdeok, et al.. (2017). Data-driven context-sensitivity for points-to analysis. Proceedings of the ACM on Programming Languages. 1(OOPSLA). 1–28. 46 indexed citations
15.
Oh, Hakjoo, Hongseok Yang, & Kwangkeun Yi. (2015). Learning a strategy for adapting a program analysis via bayesian optimisation. 572–588. 35 indexed citations
16.
Oh, Hakjoo, Wonchan Lee, Kihong Heo, Hongseok Yang, & Kwangkeun Yi. (2014). Selective context-sensitivity guided by impact pre-analysis. 475–484. 53 indexed citations
17.
Oh, Hakjoo, Kihong Heo, Wonchan Lee, et al.. (2014). Global Sparse Analysis Framework. ACM Transactions on Programming Languages and Systems. 36(3). 1–44. 12 indexed citations
18.
Oh, Hakjoo & Kwangkeun Yi. (2013). Access-based abstract memory localization in static analysis. Science of Computer Programming. 78(9). 1701–1727. 2 indexed citations
19.
Oh, Hakjoo, et al.. (2011). Access analysis-based tight localization of abstract memories. 356–370. 13 indexed citations
20.

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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