Demo Data from EDBT 2026 / reviewers in the wild / expert
Roberta Guadagni
dblp:302/7922
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-5521-9390ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › vulnerability management
vulnerability life cycle |
0.7 | 1 | 2023 | The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study · IEEE Trans. Software Eng. 2023 |
Empirical software engineering
mining software repositories |
0.2 | 1 | 2023 | The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study · IEEE Trans. Software Eng. 2023 |
Empirical software engineering › mining software repositories
vulnerability analysis |
0.2 | 1 | 2023 | The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study · IEEE Trans. Software Eng. 2023 |
Methods — techniques the papers use, named apart from their topics
manual analysis · 1.3SZZ algorithm · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | The Secret Life of Software Vulnerabilities: A Large-Scale Empirical StudyabstractSoftware vulnerabilities are weaknesses in source code that can be potentially exploited to cause loss or harm. While researchers have been devising a number of methods to deal with vulnerabilities, there is still a noticeable lack of knowledge on their software engineering life cycle, for example how vulnerabilities are introduced and removed by developers. This information can be exploited to design more effective methods for vulnerability prevention and detection, as well as to understand the granularity at which these methods should aim. To investigate the life cycle of known software vulnerabilities, we focus on how, when, and under which circumstances the contributions to theintroductionof vulnerabilities in software projects are made, as well as how long, and how they areremoved. We consider 3,663 vulnerabilities with public patches from the National Vulnerability Database—pertaining to 1,096 open-source software projects onGitHub—and define an eight-step process involving both automated parts (e.g., using a procedure based on the SZZ algorithm to find the vulnerability-contributing commits) and manual analyses (e.g., how vulnerabilities were fixed). The investigated vulnerabilities can be classified in 144 categories, take on average at least 4 contributing commits before being introduced, and half of them remain unfixed for at least more than one year. Most of thecontributionsare done by developers with high workload, often when doing maintenance activities, andremovedmostly with the addition of new source code aiming at implementing further checks on inputs. We conclude by distilling practical implications on how vulnerability detectors should work to assist developers in timely identifying these issues. Emanuele Iannone, Roberta Guadagni, Filomena Ferrucci, Andrea De Lucia, Fabio Palomba |
IEEE Trans. Software Eng. | 2 |
| 2021 | An intelligent system for focused crawling from Big Data sources
Ida Bifulco, Stefano Cirillo, Christian Esposito 0001, Roberta Guadagni, Giuseppe Polese |
Expert Syst. Appl. | 4 |