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.
Comparing and experimenting machine learning techniques for code smell detection
2015282 citationsFrancesca Arcelli Fontana, Marco Zanoni et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Marco Zanoni'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 Marco Zanoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Zanoni more than expected).
This network shows the impact of papers produced by Marco Zanoni. 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 Marco Zanoni. The network helps show where Marco Zanoni may publish in the future.
Co-authorship network of co-authors of Marco Zanoni
This figure shows the co-authorship network connecting the top 25 collaborators of Marco Zanoni.
A scholar is included among the top collaborators of Marco Zanoni 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 Marco Zanoni. Marco Zanoni 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.
Fontana, Francesca Arcelli, et al.. (2018). Poster: Machine Learning Based Code Smell Detection Through WekaNose. International Conference on Software Engineering.10 indexed citations
2.
Roveda, Riccardo, Francesca Arcelli Fontana, Ilaria Pigazzini, & Marco Zanoni. (2018). Towards an Architectural Debt Index. BOA (University of Milano-Bicocca). 408–416.31 indexed citations
3.
Palomba, Fabio, Marco Zanoni, Francesca Arcelli Fontana, Andrea De Lucia, & Rocco Oliveto. (2017). Toward a Smell-Aware Bug Prediction Model. IEEE Transactions on Software Engineering. 45(2). 194–218.79 indexed citations
Mattivi, Fulvio, et al.. (2005). Un metodo innovativo di pressatura in pressione positiva sotto gas inerte. CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach). 41(3). 77–92.3 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.