Demo Data from EDBT 2026 / reviewers in the wild / expert
Marta Han
dblp:275/9419
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
0009-0006-8774-9170ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2
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.
| Software engineering, system software, and programming languages
1 paper |
Software testing · 87% Software maintenance and evolution · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › automated testing
continuous integration testing |
0.4 | 1 | 2020 | PerfCI: A Toolchain for Automated Performance Testing during Continuous Integration of Python Projects · ASE 2020 |
Software testing
performance testing |
0.4 | 1 | 2020 | PerfCI: A Toolchain for Automated Performance Testing during Continuous Integration of Python Projects · ASE 2020 |
Software maintenance and evolution
performance regression |
0.1 | 1 | 2020 | PerfCI: A Toolchain for Automated Performance Testing during Continuous Integration of Python Projects · ASE 2020 |
Methods — techniques the papers use, named apart from their topics
performance analysis framework extension · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | PerfCI: A Toolchain for Automated Performance Testing during Continuous Integration of Python ProjectsabstractSoftware performance testing is an essential quality assurance mechanism that can identify optimization opportunities. Automating this process requires strong tool support, especially in the case of Continuous Integration (CI) where tests need to run completely automatically and it is desirable to provide developers with actionable feedback. A lack of existing tools means that performance testing is normally left out of the scope of CI. In this paper, we propose a toolchain - PerfCI - to pave the way for developers to easily set up and carry out automated performance testing under CI. Our toolchain is based on allowing users to (1) specify performance testing tasks, (2) analyze unit tests on a variety of python projects ranging from scripts to full-blown flask-based web services, by extending a performance analysis framework (VyPR) and (3) evaluate performance data to get feedback on the code. We demonstrate the feasibility of our toolchain by using it on a web service running at the Compact Muon Solenoid (CMS) experiment at the world's largest particle physics laboratory --- CERN. Omar Javed, Joshua Heneage Dawes, Marta Han, Giovanni Franzoni, Andreas Pfeiffer, Giles Reger, Walter Binder |
ASE | 3 |
| 2020 | Analysing the Performance of Python-Based Web Services with the VyPR Framework
Joshua Heneage Dawes, Marta Han, Omar Javed, Giles Reger, Giovanni Franzoni, Andreas Pfeiffer |
RV | 2 |