EDBT 2026 Demo / reviewers in the wild / expert
Kobi Feldman
dblp:359/6964
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0006-7139-1573ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 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 verification · 33% Software maintenance and evolution · 33% Program analysis · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
code readability |
0.7 | 1 | 2023 | On the Relationship between Code Verifiability and Understandability · ESEC/SIGSOFT FSE 2023 |
Program analysis › static analysis
static analysis warnings |
0.7 | 1 | 2023 | On the Relationship between Code Verifiability and Understandability · ESEC/SIGSOFT FSE 2023 |
Methods — techniques the papers use, named apart from their topics
statistical meta-analysis · 0.7correlation analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | On the Relationship between Code Verifiability and UnderstandabilityabstractProponents of software verification have argued that simpler code is easier to verify: that is, that verification tools issue fewer false positives and require less human intervention when analyzing simpler code. We empirically validate this assumption by comparing the number of warnings produced by four state-of-the-art verification tools on 211 snippets of Java code with 20 metrics of code comprehensibility from human subjects in six prior studies. Our experiments, based on a statistical (meta-)analysis, show that, in aggregate, there is a small correlation (r = 0.23) between understandability and verifiability. The results support the claim that easy-to-verify code is often easier to understand than code that requires more effort to verify. Our work has implications for the users and designers of verification tools and for future attempts to automatically measure code comprehensibility: verification tools may have ancillary benefits to understandability, and measuring understandability may require reasoning about semantic, not just syntactic, code properties. Kobi Feldman, Martin Kellogg, Oscar Chaparro |
ESEC/SIGSOFT FSE | 1 |