Demo Data from EDBT 2026 / reviewers in the wild / expert
Miguel Henriques
dblp:355/0428
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software 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.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › code smell
code smell detection |
0.7 | 1 | 2023 | Polyglot Code Smell Detection for Infrastructure as Code with GLITCH · ASE 2023 |
Software maintenance and evolution › devops
infrastructure as code |
0.7 | 1 | 2023 | Polyglot Code Smell Detection for Infrastructure as Code with GLITCH · ASE 2023 |
Systems and software security
configuration security |
0.2 | 1 | 2023 | Polyglot Code Smell Detection for Infrastructure as Code with GLITCH · ASE 2023 |
Methods — techniques the papers use, named apart from their topics
intermediate representation · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Polyglot Code Smell Detection for Infrastructure as Code with GLITCHabstractThis paper presents GLITCH, a new technology-agnostic framework that enables automated polyglot code smell detection for Infrastructure as Code scripts. GLITCH uses an intermediate representation on which different code smell detectors can be defined. It currently supports the detection of nine security smells and nine design & implementation smells in scripts written in Ansible, Chef, Docker, Puppet, or Terraform. Studies conducted with GLITCH not only show that GLITCH can reduce the effort of writing code smell analyses for multiple IaC technologies, but also that it has higher precision and recall than current state-of-the-art tools. A video describing and demonstrating GLITCH is available at: https://youtu.be/E4RhCcZjWbk. Nuno Saavedra, Miguel Henriques, João F. Ferreira 0001, Alexandra Mendes |
ASE | 3 |