ChenXi Cui

dblp:375/7073 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2024
—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% Programming languages and type systems · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › static analysis › pointer analysis
context-sensitive pointer analysis
0.812024
Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis · IEEE Trans. Software Eng. 2024
Program analysis › static analysis
pointer analysis
0.812024
Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis · IEEE Trans. Software Eng. 2024
Programming languages and type systems › programming paradigms
generic programming
0.212024
Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis · IEEE Trans. Software Eng. 2024

Methods — techniques the papers use, named apart from their topics

type variable lookup map · 0.8context customization · 0.8
YearPublicationVenuePosition
2024 Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis
abstract
Generic programming has found widespread application in object-oriented languages like Java. However, existing context-sensitive pointer analyses fail to leverage the benefits of generic programming. This paper introducesgeneric sensitivity, a new context customization scheme targeting generics. We design our context customization scheme in such a way that generic instantiation sites, i.e., locations instantiating generic classes/methods with concrete types, are always preserved as key context elements. This is realized by augmenting contexts with a type variable lookup map, which is efficiently generated in a context-sensitive manner throughout the analysis process. We have implemented various variants of generic-sensitive analysis in WALA and conducted extensive experiments to compare it with state-of-the-art approaches, including both traditional and selective context-sensitivity methods. The evaluation results demonstrate that generic sensitivity effectively enhances existing context-sensitivity approaches, striking a new balance between efficiency and precision. For instance, it enables a 1-object-sensitive analysis to achieve overall better precision compared to a 2-object-sensitive analysis, with an average speedup of 12.6 times (up to 62 times).
Haofeng Li, Tian Tan 0001, Yue Li 0006, Jie Lu 0009, Haining Meng, Liqing Cao, Yongheng Huang, Lian Li 0002, Lin Gao 0002, Peng Di, ChenXi Cui
IEEE Trans. Software Eng.12