Luming Yin

dblp:391/0569 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0004-7988-3455ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 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 · 70% Software testing · 23% Software maintenance and evolution · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › symbolic execution
dynamic symbolic execution
0.912025
Unified and Split Symbolic Execution for Exposing Semantic Differences · ACM Trans. Softw. Eng. Methodol. 2025
Program analysis › differential program analysis
semantic difference detection
0.912025
Unified and Split Symbolic Execution for Exposing Semantic Differences · ACM Trans. Softw. Eng. Methodol. 2025
Program analysis
symbolic execution
0.912025
Unified and Split Symbolic Execution for Exposing Semantic Differences · ACM Trans. Softw. Eng. Methodol. 2025
Software testing
test generation
0.912025
Unified and Split Symbolic Execution for Exposing Semantic Differences · ACM Trans. Softw. Eng. Methodol. 2025
Software maintenance and evolution
software evolution
0.312025
Unified and Split Symbolic Execution for Exposing Semantic Differences · ACM Trans. Softw. Eng. Methodol. 2025

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

symbolic execution · 0.9concolic execution · 0.9
YearPublicationVenuePosition
2025 Symbolic testing of floating-point bugs and exceptions
Dongyu Ma, Luming Yin, Hongliang Liang
J. Syst. Softw.3
2025 Unified and Split Symbolic Execution for Exposing Semantic Differences
abstract
Software evolution is an important activity during a software development lifecycle. Understanding semantic differences between two versions of a software system is a crucial yet challenging task, especially in many safety critical sectors. Consequently, various techniques have been proposed to check semantic differences between a program and its evolution. But, many current techniques are still far from being satisfactory in terms of the accuracy and efficiency. In this article, we propose a novel framework, called US 2 E , which can efficiently and effectively generate the minimal number of test cases that reveal as many semantic differences across two versions as possible. Specifically, given a unified control flow graph that denotes two versions of a program, US 2 E executes as many common nodes as possible and leaves execution of non-common nodes separately in a single concolic execution instance. We evaluate US 2 E on 86 pairs of C programs from 4 benchmarks, and experimental results show that US 2 E can efficiently and effectively generate test cases demonstrating the semantic differences across 2 versions, with better performance than 6 baseline tools.
Hongliang Liang, Luming Yin, Wenying Hu, Wuwei Shen
ACM Trans. Softw. Eng. Methodol.2