EDBT 2026 Demo / reviewers in the wild / expert
Falin Luo
dblp:367/5897
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0004-5222-8186ORCID · reported
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 testing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › fuzzing
configuration fuzzing |
0.8 | 1 | 2024 | ECFuzz: Effective Configuration Fuzzing for Large-Scale Systems · ICSE 2024 |
Software testing
configuration testing |
0.8 | 1 | 2024 | ECFuzz: Effective Configuration Fuzzing for Large-Scale Systems · ICSE 2024 |
Software testing
fuzzing |
0.2 | 1 | 2024 | ECFuzz: Effective Configuration Fuzzing for Large-Scale Systems · ICSE 2024 |
Software testing › fuzzing
mutation-based fuzzing |
0.2 | 1 | 2024 | ECFuzz: Effective Configuration Fuzzing for Large-Scale Systems · ICSE 2024 |
Methods — techniques the papers use, named apart from their topics
unit testing · 0.8mutation strategies · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ECFuzz: Effective Configuration Fuzzing for Large-Scale SystemsabstractA large-scale system contains a huge configuration space because of its large number of configuration parameters. This leads to a combination explosion among configuration parameters when exploring the configuration space. Existing configuration testing techniques first use fuzzing to generate different configuration parameters, and then directly inject them into the program under test to find configuration-induced bugs. However, they do not fully consider the complexity of large-scale systems, resulting in low testing effectiveness. In this paper, we propose ECFuzz, an effective configuration fuzzer for large-scale systems. Our core approach consists of (i) Multi-dimensional configuration generation strategy. ECFuzz first designs different mutation strategies according to different dependencies and selects multiple configuration parameters from the candidate configuration parameters to effectively generate configuration parameters; (ii) Unit-testing-oriented configuration validation strategy. ECFuzz introduces unit testing into configuration testing techniques to filter out configuration parameters that are unlikely to yield errors before executing system testing, and effectively validate generated configuration parameters. We have conducted extensive experiments in real-world large-scale systems including HCommon, HDFS, HBase, ZooKeeper and Alluxio. Our evaluation shows that ECFuzz is effective in finding configuration-induced crash bugs. Compared with the state-of-the-art configuration testing tools including ConfTest, ConfErr and ConfDiagDetector, ECFuzz finds 60.3--67 more unexpected failures when the same 1000 testcases are injected into the system with an increase of 1.87x--2.63x. Moreover, ECFuzz has exposed 14 previously unknown bugs, and 5 of them have been confirmed. Senyi Li, Keyao Li, Falin Luo, Hong-Fang Yu, Shanshan Li 0001, Xiang Li 0078 |
ICSE | 4 |