Andrea Stocco

2.0k citations
46 papers · 1.3k indexed · 1 hit paper · h-index 21

Andrea Stocco

42 papers receiving 1.2k citations

Hit Papers

Taxonomy of real faults in deep learning systems192202020262022202450100150

Peers

Andrea Stocco
Comparison fields: 5 of 60
  • Software 716
  • Information Systems 602
  • Signal Processing 178
  • Computer Networks and Communications 329
  • Automotive Engineering 157
Replace Paolo Arcaini with:
Paolo Arcaini Japan
Kexin Pei United States
Alessio Gambi Germany
Derek Rayside Canada
Robyn R. Lutz United States
Hussein Zedan United Kingdom
Joshua Garcia United States
Martin Leucker Germany
Qi Alfred Chen United States
Vittoria Nardone Italy
Andrea Stocco relative to Paolo Arcaini Japan Paolo Arcaini's profile →
Citations per field
00.5×1.5×2.3×
Paolo Arcaini · 1×
Citations per year

Countries citing papers authored by Andrea Stocco

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Stocco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Andrea Stocco, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Andrea Stocco Line = papers co-authored together Andrea Stocco links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20250
3 20250
4 20249
5 202320
6 202230
7 202146
8 202022
9 202096
10 202012
11 202015
12
Chapter Three - Three Open Problems in the Context of E2E Web Testing and a Vision: NEONATE.
20191
13
E2E Web Test Dependency Detection using NLP.
20191
14 201954
15 201941
16 201640
17 201555
18 201421
19 20135
20 200850

About Andrea Stocco

Andrea Stocco is a scholar working on Software, Information Systems and Automotive Engineering, having authored 46 papers that have together received 1.3k indexed citations. Recurring topics across this work include Software Testing and Debugging Techniques (29 papers), Software Engineering Research (16 papers), Software System Performance and Reliability (10 papers), Adversarial Robustness in Machine Learning (9 papers), Software Reliability and Analysis Research (8 papers), Web Data Mining and Analysis (8 papers), Autonomous Vehicle Technology and Safety (7 papers) and Web Application Security Vulnerabilities (6 papers). The work is most often cited by research in Software (716 citations), Information Systems (602 citations) and Signal Processing (178 citations). Andrea Stocco has collaborated with scholars based in Italy, Switzerland and Canada. Frequent co-authors include Paolo Tonella, Filippo Ricca, Gunel Jahangirova, Maurizio Leotta, Vincenzo Riccio, Nargiz Humbatova, Michael Weiß, Ali Mesbah, Gabriele Bavota and R. Caldon. Their work appears in journals such as Behaviour Research and Therapy, IEEE Transactions on Software Engineering and Electric Power Systems Research.

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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