Laurie Williams

16.7k total citations · 2 hit papers
361 papers, 10.4k citations indexed

About

Laurie Williams is a scholar working on Information Systems, Software and Computer Science Applications. According to data from OpenAlex, Laurie Williams has authored 361 papers receiving a total of 10.4k indexed citations (citations by other indexed papers that have themselves been cited), including 309 papers in Information Systems, 132 papers in Software and 66 papers in Computer Science Applications. Recurrent topics in Laurie Williams's work include Software Engineering Research (239 papers), Software Engineering Techniques and Practices (134 papers) and Software Reliability and Analysis Research (110 papers). Laurie Williams is often cited by papers focused on Software Engineering Research (239 papers), Software Engineering Techniques and Practices (134 papers) and Software Reliability and Analysis Research (110 papers). Laurie Williams collaborates with scholars based in United States, United Kingdom and Switzerland. Laurie Williams's co-authors include Robert R. Kessler, Andrew Meneely, Yonghee Shin, Nachiappan Nagappan, Alistair Cockburn, Akond Rahman, Jason A. Osborne, Lucas Layman, Mladen A. Vouk and Carol Miller and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Communications of the ACM.

In The Last Decade

Laurie Williams

348 papers receiving 9.4k citations

Hit Papers

Strengthening the case for pair programming 2000 2026 2008 2017 2000 2010 100 200 300 400 500
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. Strengthening the case for pair programming
    2000 523 citations Laurie Williams et al. profile →
  2. Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities
    2010 408 citations Andrew Meneely, Laurie Williams 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
Laurie Williams United States 54 8.0k 3.6k 2.7k 1.7k 1.4k 361 10.4k
Nachiappan Nagappan United States 48 8.6k 1.1× 5.2k 1.5× 1.7k 0.6× 3.6k 2.1× 1.5k 1.1× 166 10.5k
Per Runeson Sweden 38 9.5k 1.2× 4.5k 1.2× 1.8k 0.7× 2.3k 1.3× 3.1k 2.2× 176 12.7k
Martin Höst Sweden 25 7.6k 1.0× 2.9k 0.8× 1.6k 0.6× 1.8k 1.0× 2.7k 1.9× 105 10.2k
Jeannette M. Wing United States 31 3.4k 0.4× 1.3k 0.4× 5.5k 2.0× 3.2k 1.8× 2.9k 2.0× 102 12.2k
Andrea De Lucia Italy 59 9.8k 1.2× 6.5k 1.8× 1.2k 0.4× 2.3k 1.3× 2.4k 1.7× 325 11.5k
John Grundy Australia 46 5.6k 0.7× 1.7k 0.5× 732 0.3× 2.8k 1.6× 2.7k 1.9× 574 9.0k
Björn Regnell Sweden 30 6.1k 0.8× 2.1k 0.6× 1.2k 0.4× 1.3k 0.7× 2.5k 1.7× 99 7.8k
Philip J. Guo United States 35 2.5k 0.3× 1.4k 0.4× 2.3k 0.8× 680 0.4× 1.3k 0.9× 93 5.7k
Mario Piattini Spain 44 5.7k 0.7× 1.4k 0.4× 811 0.3× 1.6k 0.9× 2.5k 1.7× 639 8.3k
Amy J. Ko United States 43 2.3k 0.3× 1.4k 0.4× 2.4k 0.9× 506 0.3× 894 0.6× 153 5.4k

Countries citing papers authored by Laurie Williams

Since Specialization
Citations

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

Fields of papers citing papers by Laurie Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laurie Williams

This figure shows the co-authorship network connecting the top 25 collaborators of Laurie Williams. A scholar is included among the top collaborators of Laurie Williams 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 Laurie Williams. Laurie Williams 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.
2.
Williams, Laurie, et al.. (2024). MalwareBench: Malware samples are not enough. 728–732. 1 indexed citations
3.
Ajmeri, Nirav, et al.. (2022). Feature toggles as code: Heuristics and metrics for structuring feature toggles. Information and Software Technology. 145. 106813–106813. 7 indexed citations
4.
Rahman, Akond, Md Rayhanur Rahman, Chris Parnin, & Laurie Williams. (2021). Security Smells in Ansible and Chef Scripts. ACM Transactions on Software Engineering and Methodology. 30(1). 1–31. 45 indexed citations
5.
Rahman, Akond & Laurie Williams. (2021). Different Kind of Smells: Security Smells in Infrastructure as Code Scripts. IEEE Security & Privacy. 19(3). 33–41. 16 indexed citations
6.
Rahman, Md Rayhanur, Akond Rahman, & Laurie Williams. (2019). Share, But be Aware: Security Smells in Python Gists. 536–540. 18 indexed citations
7.
Munaiah, Nuthan, et al.. (2019). Characterizing Attacker Behavior in a Cybersecurity Penetration Testing Competition. 1–6. 18 indexed citations
8.
Rahman, Akond, et al.. (2018). A systematic mapping study of infrastructure as code research. Information and Software Technology. 108. 65–77. 85 indexed citations
9.
Rahman, Akond, et al.. (2017). Which factors influence practitioners' usage of build automation tools?. International Conference on Software Engineering. 20–26. 9 indexed citations
10.
Herzig, Kim, et al.. (2015). Approximating attack surfaces with stack traces. International Conference on Software Engineering. 2. 199–208. 28 indexed citations
11.
Morrison, Patrick, et al.. (2013). Proposing regulatory-driven automated test suites for electronic health record systems. 46–49. 2 indexed citations
12.
Zimmermann, Thomas, Nachiappan Nagappan, Laurie Williams, Kim Herzig, & Rahul Premraj. (2011). An Empirical Study of the Factors Relating Field Failures and Dependencies.
13.
Layman, Lucas, et al.. (2008). Addressing diverse needs through a balance of agile and plan-driven software development methodologies in the core software engineering course. International journal of engineering education. 24(4). 659–670. 17 indexed citations
14.
Williams, Laurie, et al.. (2007). A Longitudinal Study of the Use of a Test-Driven Development Practice in Industry. 9(8). 9 indexed citations
15.
Gegick, Michael, et al.. (2006). An early testing and defense web application framework for malicious input attacks. The Journal of Chemical Physics. 135(2). 24104–24104. 5 indexed citations
16.
Srikanth, Hema, Laurie Williams, & J. Osborne. (2005). System test case prioritization of new and regression test cases. 62–71. 127 indexed citations
17.
Dagnino, Aldo, et al.. (2004). Experiences in applying agile software development practices in new product development.. 501–506. 10 indexed citations
18.
Williams, Laurie, et al.. (2002). A Video-Enhanced Environment for Distributed Extreme Programming. 4 indexed citations
19.
20.
Cockburn, Alistair & Laurie Williams. (2001). The costs and benefits of pair programming. Addison-Wesley Longman Publishing Co., Inc. eBooks. 223–243. 261 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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