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Murad Akhundov

dblp:303/9044 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Program analysis › static analysis
datalog-based analysis
0.712023
Annotative Software Product Line Analysis Using Variability-Aware Datalog · IEEE Trans. Software Eng. 2023
Program analysis
static analysis
0.712023
Annotative Software Product Line Analysis Using Variability-Aware Datalog · IEEE Trans. Software Eng. 2023
Requirements engineering and software design
software product lines
0.212023
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
YearPublicationVenuePosition
2023 Annotative Software Product Line Analysis Using Variability-Aware Datalog
abstract
Applying 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
ATVA1