David Bowes

3.2k total citations · 2 hit papers
58 papers, 2.2k citations indexed

About

David Bowes is a scholar working on Information Systems, Software and Computer Networks and Communications. According to data from OpenAlex, David Bowes has authored 58 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Information Systems, 48 papers in Software and 9 papers in Computer Networks and Communications. Recurrent topics in David Bowes's work include Software Engineering Research (54 papers), Software Reliability and Analysis Research (43 papers) and Software Testing and Debugging Techniques (31 papers). David Bowes is often cited by papers focused on Software Engineering Research (54 papers), Software Reliability and Analysis Research (43 papers) and Software Testing and Debugging Techniques (31 papers). David Bowes collaborates with scholars based in United Kingdom, Italy and Ireland. David Bowes's co-authors include Tracy Hall, David Gray, Steve Counsell, Sarah Beecham, Martin Shepperd, Jean Petrić, Yi Sun, Bruce Christianson, Norman E. Davey and Min Zhang and has published in prestigious journals such as IEEE Transactions on Software Engineering, IEEE Software and Journal of Systems and Software.

In The Last Decade

David Bowes

56 papers receiving 2.2k citations

Hit Papers

A Systematic Literature Review on Fault Prediction Perfor... 2011 2026 2016 2021 2011 2014 250 500 750
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. A Systematic Literature Review on Fault Prediction Performance in Software Engineering
    2011 823 citations Tracy Hall, Sarah Beecham et al. IEEE Transactions on Software Engineering profile →
  2. Researcher Bias: The Use of Machine Learning in Software Defect Prediction
    2014 260 citations Martin Shepperd, David Bowes et al. IEEE Transactions on Software Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Bowes United Kingdom 21 2.1k 1.8k 629 458 131 58 2.2k
Naoyasu Ubayashi Japan 17 1.4k 0.7× 1.0k 0.6× 505 0.8× 443 1.0× 104 0.8× 109 1.6k
Sunghun Kim United States 23 2.4k 1.2× 1.9k 1.1× 777 1.2× 352 0.8× 388 3.0× 43 2.6k
Ferdian Thung Singapore 26 1.5k 0.7× 934 0.5× 464 0.7× 474 1.0× 337 2.6× 64 1.8k
Uirá Kulesza Brazil 20 1.5k 0.7× 600 0.3× 478 0.8× 1.1k 2.5× 65 0.5× 125 1.7k
Abdelwahab Hamou‐Lhadj Canada 23 1.1k 0.5× 659 0.4× 926 1.5× 623 1.4× 410 3.1× 140 1.7k
Ladan Tahvildari Canada 22 1.5k 0.7× 751 0.4× 994 1.6× 1.1k 2.4× 85 0.6× 81 2.1k
G.A. Di Lucca Italy 22 1.3k 0.6× 753 0.4× 429 0.7× 455 1.0× 195 1.5× 89 1.6k
Norman Wilde United States 17 1.2k 0.6× 702 0.4× 445 0.7× 580 1.3× 84 0.6× 66 1.4k
Vincent J. Hellendoorn United States 15 812 0.4× 535 0.3× 235 0.4× 436 1.0× 194 1.5× 26 1.2k
Elliot J. Chikofsky United States 8 1.1k 0.5× 555 0.3× 307 0.5× 641 1.4× 105 0.8× 14 1.4k

Countries citing papers authored by David Bowes

Since Specialization
Citations

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

Fields of papers citing papers by David Bowes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Bowes

This figure shows the co-authorship network connecting the top 25 collaborators of David Bowes. A scholar is included among the top collaborators of David Bowes 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 David Bowes. David Bowes 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.
Bowes, David, et al.. (2022). Improved Evaluation of Automatic Source Code Summarisation. 326–335.
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Bowes, David, et al.. (2020). Exploiting fault localisation for efficient program repair. 311–312. 1 indexed citations
5.
Petrić, Jean, Tracy Hall, & David Bowes. (2020). Which Software Faults Are Tests Not Detecting?. 160–169. 2 indexed citations
6.
Ortu, Marco, Andrea Pinna, Roberto Tonelli, et al.. (2018). Angry-builds. 1–2. 1 indexed citations
7.
Destefanis, Giuseppe, Marco Ortu, David Bowes, Michele Marchesi, & Roberto Tonelli. (2018). On measuring affects of github issues' commenters. 14–19. 24 indexed citations
8.
Bowes, David, et al.. (2017). How good are my tests. 9–14. 17 indexed citations
9.
Kırbaş, Serkan, Bora Çağlayan, Tracy Hall, et al.. (2017). The relationship between evolutionary coupling and defects in large industrial software. Journal of Software Evolution and Process. 29(4). 25 indexed citations
10.
Counsell, Steve, et al.. (2017). Assert Use and Defectiveness in Industrial Code. arXiv (Cornell University). 25. 20–23. 1 indexed citations
11.
Bowes, David, et al.. (2017). Getting Defect Prediction Into Industrial Practice: the ELFF Tool. 30. 44–47. 7 indexed citations
12.
Hall, Tracy, David Bowes, Steve Counsell, Leon Moonen, & Aiko Yamashita. (2015). Software fault characteristics: A synthesis of the literature. Brunel University Research Archive (BURA) (Brunel University London). 1 indexed citations
13.
Bowes, David, et al.. (2015). What is the Impact of Imbalance on Software Defect Prediction Performance?. 1–4. 32 indexed citations
14.
Hall, Tracy, Min Zhang, David Bowes, & Yi Sun. (2014). Some Code Smells Have a Significant but Small Effect on Faults. ACM Transactions on Software Engineering and Methodology. 23(4). 1–39. 118 indexed citations
15.
Shepperd, Martin, David Bowes, & Tracy Hall. (2014). Researcher Bias: The Use of Machine Learning in Software Defect Prediction. IEEE Transactions on Software Engineering. 40(6). 603–616. 260 indexed citations breakdown →
16.
Bowes, David, Tracy Hall, & David Gray. (2012). Comparing the performance of fault prediction models which report multiple performance measures. University of Hertfordshire Research Archive (University of Hertfordshire). 109–118. 42 indexed citations
17.
Bowes, David, et al.. (2012). A mapping study of software code cloning. University of Hertfordshire Research Archive (University of Hertfordshire). 274–278. 3 indexed citations
18.
Gray, David, David Bowes, Norman E. Davey, Yi Sun, & Bruce Christianson. (2011). The misuse of the NASA Metrics Data Program data sets for automated software defect prediction. University of Hertfordshire Research Archive (University of Hertfordshire). 96–103. 131 indexed citations
19.
Hall, Tracy, Sarah Beecham, David Bowes, David Gray, & Steve Counsell. (2011). A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Transactions on Software Engineering. 38(6). 1276–1304. 823 indexed citations breakdown →
20.
Counsell, Steve, et al.. (2010). An Analysis of the "Inconclusive' Change Report Category in OSS Assisted by a Program Slicing Metric. University of Hertfordshire Research Archive (University of Hertfordshire). 16. 283–286. 3 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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