most citedAI Chain on Large Language Model for Unsupervised Control Flow Graph Generation for Statically-Typed Partial Code

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.SE2023

Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference

Qing Huang, Yanbang Sun, Zhenchang Xing +5

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitat…

cs.SE2023

Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain

Qing Huang, Zhenyu Wan, Zhenchang Xing +4

API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge gr…

cs.SE20231 cited

AI Chain on Large Language Model for Unsupervised Control Flow Graph Generation for Statically-Typed Partial Code

Qing Huang, Zhou Zou, Zhenchang Xing +3

Control Flow Graphs (CFGs) are essential for visualizing, understanding and analyzing program behavior. For statically-typed programming language like Java, developers obtain CFGs…

cs.SE2023

API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model

Qing Huang, Yanbang Sun, Zhenchang Xing +3

Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering ta…

cs.SE2022

Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code

Qing Huang, Zhiqiang Yuan, Zhenchang Xing +3

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects…