Demo Data from EDBT 2026 / reviewers in the wild / expert
Matthew Blanco
dblp:337/0584
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—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 |
Program analysis · 87% Runtime systems and virtual machines · 13% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › information flow tracking
dynamic taint analysis |
0.6 | 1 | 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022 |
Systems and software security › vulnerability analysis
injection vulnerability detection |
0.6 | 1 | 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022 |
Program analysis
dynamic analysis |
0.6 | 1 | 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022 |
Program analysis › static analysis
taint analysis |
0.6 | 1 | 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022 |
Runtime systems and virtual machines
language runtime |
0.2 | 1 | 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022 |
Methods — techniques the papers use, named apart from their topics
abstract machine · 1.1VM-supported instrumentation · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Augur: Dynamic Taint Analysis for Asynchronous JavaScriptabstractDynamic taint analysis (DTA) is a popular approach to help protect JavaScript applications against injection vulnerabilities. In 2016, the ECMAScript 7 JavaScript language standard introduced many language features that most existing DTA tools for JavaScript do not support, e.g., the async/await keywords for asynchronous programming. We present Augur, a high-performance dynamic taint analysis for ES7 JavaScript that leverages VM-supported instrumentation. Integrating directly with a public, stable instrumentation API gives Augur the ability to run with high performance inside the VM and remain resilient to language revisions. We extend the abstract-machine approach to DTA to handle asynchronous function calls. In addition to providing the classic DTA use case of injection vulnerability detection, Augur is highly configurable to support any type of taint analysis, making it useful outside of the security domain. We evaluated Augur on a set of 20 benchmarks, and observed a median runtime overhead of only 1.77 ×, a median performance improvement of 298% compared to the previous state-of-the-art. Mark W. Aldrich, Alexi Turcotte, Matthew Blanco, Frank Tip |
ASE | 3 |