Xiaohu Song

dblp:86/7414 · DBLP profile ↗
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5ranked-venue papers
4as first author
3since 2021 · last 2026
—ORCID · conflict

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

Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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
Software maintenance and evolution · 62% Program analysis · 38%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › software ecosystems
dependency management
0.812024
Efficiently Trimming the Fat: Streamlining Software Dependencies with Java Reflection and Dependency Analysis · ICSE 2024
Program analysis › static analysis
reflection analysis
0.212024
Efficiently Trimming the Fat: Streamlining Software Dependencies with Java Reflection and Dependency Analysis · ICSE 2024
Program analysis
static analysis
0.212024
Efficiently Trimming the Fat: Streamlining Software Dependencies with Java Reflection and Dependency Analysis · ICSE 2024

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

java reflection analysis · 0.8dependency analysis · 0.8
YearPublicationVenuePosition
2026 Selecting test cases to reveal defects in recurrent neural networks
Xiaohu Song, Hai Yu 0001, Zhiliang Zhu 0001
Neurocomputing2
2025 Debloating Software Through Enhanced Static Analysis and Constraint Rules
abstract
ABSTRACT Introduction Java applications often bloat, consuming more resources than necessary. Existing bytecode debloating techniques have several limitations, such as compromised correctness caused by incomplete code collection, which leads to false positives. The debloating process is resource‐intensive because it relies significantly on the test coverage to identify unnecessary code. This method is particularly problematic for large‐enterprise applications, where executing a single test case can take hours or days. In addition, most available debloating tools are standalone utilities that require manual configuration to be integrated with the project‐building process. Methods In this study, we introduce an automated approach known as Trimming. Trimming achieves three main improvements: (1) it collects the necessary code using enhanced static analysis, improves the modeling of reflective calls, and infers instantiation objects. It also uses constraint rules to identify and process reference codes that contribute to bloating but cannot be removed without causing errors. (2) It is not based on test coverage. (3) It allows for direct debloating from the project build by implementing a Maven plugin that is configured within the POM.xml file of the bloated project. Results Our evaluation results show that when Java reflection analysis or constraint rules in TRIMMING were disabled, the effectiveness of debloating analysis dropped to 88.25% with a 95% confidence interval (CI) of [85.3%, 91.2%], and 35.0% with a 95% CI of [30.5%, 39.5%], respectively. Moreover, inferring instantiation objects reduced the program size by 4.9%. Trimming achieved a bytecode reduction rate of 24.1%, with all the debloated projects successfully compiling and passing test suites.
Xiaohu Song, Hai Yu 0001, Ying Wang 0038, Zhiliang Zhu 0001
Softw. Pract. Exp.1
2024 Efficiently Trimming the Fat: Streamlining Software Dependencies with Java Reflection and Dependency Analysis
abstract
Numerous third-party libraries introduced into client projects are not actually required, resulting in modern software being gradually bloated. Software developers may spend much unnecessary effort to manage the bloated dependencies: keeping the library versions up-to-date, making sure that heterogeneous licenses are compatible, and resolving dependency conflict or vulnerability issues.
Xiaohu Song, Ying Wang 0038, Guangtai Liang, Qianxiang Wang, Zhiliang Zhu 0001
ICSE1
2013 Affine transforms between image space and color space for invariant local descriptors
Xiaohu Song, Damien Muselet, Alain Trémeau
Pattern Recognit.1
2009 Local Color Descriptor for Object Recognition across Illumination Changes
Xiaohu Song, Damien Muselet, Alain Trémeau
ACIVS1