Kihong Heo

845 total citations
29 papers, 495 citations indexed

About

Kihong Heo is a scholar working on Software, Artificial Intelligence and Information Systems. According to data from OpenAlex, Kihong Heo has authored 29 papers receiving a total of 495 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Software, 20 papers in Artificial Intelligence and 14 papers in Information Systems. Recurrent topics in Kihong Heo's work include Software Testing and Debugging Techniques (21 papers), Software Engineering Research (14 papers) and Formal Methods in Verification (10 papers). Kihong Heo is often cited by papers focused on Software Testing and Debugging Techniques (21 papers), Software Engineering Research (14 papers) and Formal Methods in Verification (10 papers). Kihong Heo collaborates with scholars based in South Korea, United States and United Kingdom. Kihong Heo's co-authors include Hakjoo Oh, Mayur Naik, Woosuk Lee, Kwangkeun Yi, Wonchan Lee, Hongseok Yang, Mukund Raghothaman, Rajeev Alur, Xujie Si and Tianyi Chen and has published in prestigious journals such as ACM SIGPLAN Notices, ACM Transactions on Programming Languages and Systems and Software Practice and Experience.

In The Last Decade

Kihong Heo

28 papers receiving 490 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kihong Heo South Korea	13	307	274	257	178	87	29	495
Yoav Zibin Israel	7	200 0.7×	196 0.7×	225 0.9×	113 0.6×	156 1.8×	17	443
Maria Christakis Germany	11	239 0.8×	200 0.7×	127 0.5×	123 0.7×	52 0.6×	26	393
Max Schäfer United States	15	393 1.3×	510 1.9×	216 0.8×	232 1.3×	146 1.7×	30	660
Ravi Chugh United States	9	97 0.3×	207 0.8×	246 1.0×	148 0.8×	78 0.9×	21	382
Aditya Kanade India	9	220 0.7×	148 0.5×	112 0.4×	68 0.4×	104 1.2×	16	377
Hila Peleg Israel	9	221 0.7×	192 0.7×	115 0.4×	111 0.6×	46 0.5×	15	327
Juan Pablo Galeotti Argentina	14	468 1.5×	345 1.3×	121 0.5×	89 0.5×	106 1.2×	41	557
Sergej Schumilo Germany	6	336 1.1×	156 0.6×	154 0.6×	293 1.6×	74 0.9×	6	462
Sylvain Lebresne United States	5	130 0.4×	265 1.0×	225 0.9×	111 0.6×	79 0.9×	6	400
Avik Chaudhuri United States	11	220 0.7×	350 1.3×	334 1.3×	321 1.8×	169 1.9×	23	567

Countries citing papers authored by Kihong Heo

Since Specialization

Citations

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

Fields of papers citing papers by Kihong Heo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kihong Heo

This figure shows the co-authorship network connecting the top 25 collaborators of Kihong Heo. A scholar is included among the top collaborators of Kihong Heo 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 Kihong Heo. Kihong Heo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Heo, Kihong, et al.. (2024). Evaluating Directed Fuzzers: Are We Heading in the Right Direction?. Proceedings of the ACM on software engineering.. 1(FSE). 316–337. 1 indexed citations

Heo, Kihong, et al.. (2024). Translation Validation for JIT Compiler in the V8 JavaScript Engine. 1–12. 2 indexed citations

Kim, Hyun‐Su, Mukund Raghothaman, & Kihong Heo. (2022). Learning probabilistic models for static analysis alarms. 1282–1293. 6 indexed citations

Heo, Kihong, et al.. (2022). TRACER. Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 1695–1708. 13 indexed citations

Machiry, Aravind, et al.. (2022). PacJam. 903–916. 4 indexed citations

Heo, Kihong, Mukund Raghothaman, Xujie Si, & Mayur Naik. (2019). Continuously reasoning about programs using differential Bayesian inference. 561–575. 14 indexed citations

Heo, Kihong, et al.. (2018). Effective Program Debloating via Reinforcement Learning. 380–394. 78 indexed citations

Raghothaman, Mukund, et al.. (2018). User-guided program reasoning using Bayesian inference. 722–735. 34 indexed citations

Lee, Woosuk, Kihong Heo, Rajeev Alur, & Mayur Naik. (2018). Accelerating search-based program synthesis using learned probabilistic models. 436–449. 47 indexed citations

10.

Lee, Woosuk, Kihong Heo, Rajeev Alur, & Mayur Naik. (2018). Accelerating search-based program synthesis using learned probabilistic models. ACM SIGPLAN Notices. 53(4). 436–449. 18 indexed citations

11.

Heo, Kihong, Hakjoo Oh, Hongseok Yang, & Kwangkeun Yi. (2018). Adaptive Static Analysis via Learning with Bayesian Optimization. ACM Transactions on Programming Languages and Systems. 40(4). 1–37. 3 indexed citations

12.

Oh, Hakjoo, et al.. (2017). Automatically generating features for learning program analysis heuristics for C-like languages. Proceedings of the ACM on Programming Languages. 1(OOPSLA). 1–25. 16 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.

Lee, Woosuk, et al.. (2017). Sound Non-Statistical Clustering of Static Analysis Alarms. ACM Transactions on Programming Languages and Systems. 39(4). 1–35. 12 indexed citations

15.

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

16.

Heo, Kihong, Hakjoo Oh, & Kwangkeun Yi. (2017). Machine-Learning-Guided Selectively Unsound Static Analysis. 519–529. 39 indexed citations

17.

Heo, Kihong, et al.. (2015). Widening with thresholds via binary search. Software Practice and Experience. 46(10). 1317–1328. 1 indexed citations

18.

Oh, Hakjoo, Wonchan Lee, Kihong Heo, Hongseok Yang, & Kwangkeun Yi. (2014). Selective context-sensitivity guided by impact pre-analysis. 475–484. 53 indexed citations

19.

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

20.

Oh, Hakjoo, Kihong Heo, Wonchan Lee, Woosuk Lee, & Kwangkeun Yi. (2012). Design and implementation of sparse global analyses for C-like languages. 229–238. 53 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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