EDBT 2026 Demo / reviewers in the wild / expert
Murad Akhundov
dblp:303/9044
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2023
0000-0002-7586-6680ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 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% Requirements engineering and software design · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › static analysis
datalog-based analysis |
0.7 | 1 | 2023 | Annotative Software Product Line Analysis Using Variability-Aware Datalog · IEEE Trans. Software Eng. 2023 |
Program analysis
static analysis |
0.7 | 1 | 2023 | Annotative Software Product Line Analysis Using Variability-Aware Datalog · IEEE Trans. Software Eng. 2023 |
Requirements engineering and software design
software product lines |
0.2 | 1 | 2023 | Annotative Software Product Line Analysis Using Variability-Aware Datalog · IEEE Trans. Software Eng. 2023 |
Methods — techniques the papers use, named apart from their topics
lifted inference · 0.7datalog · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Annotative Software Product Line Analysis Using Variability-Aware DatalogabstractApplying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to “lift” particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Soufflé Datalog engine. We evaluate our implementation on a set of Java and C-language benchmark annotative software product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually. Ramy Shahin, Murad Akhundov, Marsha Chechik |
IEEE Trans. Software Eng. | 2 |
| 2021 | Verification by Gambling on Program Slices
Murad Akhundov, Federico Mora 0002, Nick Feng, Vincent Hui, Marsha Chechik |
ATVA | 1 |