Kim Herzig

2.0k total citations · 1 hit paper
37 papers, 1.4k citations indexed

About

Kim Herzig is a scholar working on Information Systems, Software and Computer Networks and Communications. According to data from OpenAlex, Kim Herzig has authored 37 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Information Systems, 25 papers in Software and 21 papers in Computer Networks and Communications. Recurrent topics in Kim Herzig's work include Software Engineering Research (30 papers), Software Reliability and Analysis Research (21 papers) and Software System Performance and Reliability (21 papers). Kim Herzig is often cited by papers focused on Software Engineering Research (30 papers), Software Reliability and Analysis Research (21 papers) and Software System Performance and Reliability (21 papers). Kim Herzig collaborates with scholars based in United States, United Kingdom and Germany. Kim Herzig's co-authors include Andreas Zeller, Sascha Just, Thomas Brendan Murphy, Nachiappan Nagappan, Rahul Premraj, Jacek Czerwonka, Laurie Williams, Michaela Greiler, Thomas Zimmermann and Patrick Morrison and has published in prestigious journals such as Empirical Software Engineering, USENIX Security Symposium and Figshare.

In The Last Decade

Kim Herzig

34 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

It's not a bug, it's a feature: How misclassification impacts bug prediction

2013 215 citations Kim Herzig, Sascha Just et al. 2013 35th International Conference on Software Engineering (ICSE) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kim Herzig United States	20	1.3k	1.0k	441	258	207	37	1.4k
Laurence Duchien France	13	1.0k 0.8×	562 0.5×	453 1.0×	181 0.7×	483 2.3×	44	1.2k
Foyzur Rahman United States	14	1.2k 0.9×	890 0.8×	379 0.9×	150 0.6×	134 0.6×	15	1.2k
J. David Morgenthaler United States	13	760 0.6×	558 0.5×	209 0.5×	200 0.8×	207 1.0×	18	881
John C. Munson United States	18	1.3k 1.0×	1.2k 1.1×	361 0.8×	71 0.3×	313 1.5×	63	1.5k
Alessandro Marchetto Italy	19	884 0.7×	742 0.7×	321 0.7×	98 0.4×	253 1.2×	82	1.1k
Elmar Juergens Germany	14	929 0.7×	630 0.6×	186 0.4×	216 0.8×	205 1.0×	41	1.0k
Rohit Gheyi Brazil	21	1.2k 0.9×	873 0.8×	318 0.7×	130 0.5×	647 3.1×	102	1.4k
Árpád Beszédes Hungary	18	934 0.7×	857 0.8×	302 0.7×	182 0.7×	301 1.5×	94	1.2k
Nathaniel Ayewah United States	9	639 0.5×	507 0.5×	156 0.4×	213 0.8×	153 0.7×	12	779
Raymond P.L. Buse United States	11	767 0.6×	431 0.4×	202 0.5×	111 0.4×	249 1.2×	11	871

Countries citing papers authored by Kim Herzig

Since Specialization

Citations

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

Fields of papers citing papers by Kim Herzig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kim Herzig

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zhang, Jiyang, et al.. (2023). Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft. 162–172. 4 indexed citations

2.

Sajnani, Hitesh, et al.. (2021). A Case Study of Onboarding in Software Teams: Tasks and Strategies. 613–623. 23 indexed citations

3.

Just, Sascha, Kim Herzig, Jacek Czerwonka, & Thomas Brendan Murphy. (2016). Switching to Git: The Good, the Bad, and the Ugly. 400–411. 8 indexed citations

4.

Herzig, Kim, et al.. (2015). Approximating attack surfaces with stack traces. International Conference on Software Engineering. 2. 199–208. 28 indexed citations

5.

Herzig, Kim & Nachiappan Nagappan. (2015). Empirically detecting false test alarms using association rules. International Conference on Software Engineering. 2. 39–48. 36 indexed citations

6.

Herzig, Kim & Nachiappan Nagappan. (2015). The Impact of Test Ownership and Team Structure on the Reliability, Effectiveness of Quality Test Runs.. 105–106. 1 indexed citations

7.

Herzig, Kim, Michaela Greiler, Jacek Czerwonka, & Thomas Brendan Murphy. (2015). The art of testing less without sacrificing quality. International Conference on Software Engineering. 1. 483–493. 45 indexed citations

8.

Herzig, Kim, et al.. (2015). Approximating Attack Surfaces with Stack Traces. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. 199–208. 25 indexed citations

9.

Herzig, Kim, Michaela Greiler, Jacek Czerwonka, & Thomas Brendan Murphy. (2015). The Art of Testing Less without Sacrificing Quality. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. 483–493. 60 indexed citations

10.

Morrison, Patrick, Kim Herzig, Thomas Brendan Murphy, & Laurie Williams. (2015). Challenges with applying vulnerability prediction models. 1–9. 73 indexed citations

11.

Greiler, Michaela, Kim Herzig, & Jacek Czerwonka. (2015). Code Ownership and Software Quality: A Replication Study. 2–12. 29 indexed citations

12.

Herzig, Kim & Nachiappan Nagappan. (2014). The impact of test ownership and team structure on the reliability and effectiveness of quality test runs. 1–10. 14 indexed citations

13.

Herzig, Kim, Sascha Just, & Andreas Zeller. (2013). It's not a bug, it's a feature: How misclassification impacts bug prediction. 2013 35th International Conference on Software Engineering (ICSE). 392–401. 215 indexed citations breakdown →

14.

Herzig, Kim, et al.. (2012). Fuzzing with code fragments. USENIX Security Symposium. 38–38. 146 indexed citations

15.

Zimmermann, Thomas, Nachiappan Nagappan, Laurie Williams, Kim Herzig, & Rahul Premraj. (2011). An Empirical Study of the Factors Relating Field Failures and Dependencies.

16.

Nagappan, Nachiappan, et al.. (2011). An Empirical Study on the Relation between Dependency Neighborhoods and Failures. 347–356. 19 indexed citations

17.

Herzig, Kim & Andreas Zeller. (2011). Mining Cause-Effect-Chains from Version Histories. 60–69. 20 indexed citations

18.

Herzig, Kim & Andreas Zeller. (2011). Mining Your Own Evidence.. 517–530. 4 indexed citations

19.

Nagappan, Nachiappan, Andreas Zeller, Thomas Zimmermann, Kim Herzig, & Thomas Brendan Murphy. (2010). Change Bursts as Defect Predictors. 309–318. 151 indexed citations

20.

Herzig, Kim & Andreas Zeller. (2009). Mining the Jazz repository: Challenges and opportunities. 159–162. 12 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.

Explore authors with similar magnitude of impact