activity
20232026
most citedThe Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

10 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation

Binquan Zhang, Li Zhang, Lin Shi +6

Large Language Models (LLMs) have revolutionized code generation, evolving from static tools into dynamic conversational interfaces that facilitate complex, multi-turn collaborativ…

cs.CV2025

DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation

Ziyu Zhao, Xiaoguang Li, Linjia Shi +2

Open-vocabulary semantic segmentation aims to segment images into distinct semantic regions for both seen and unseen categories at the pixel level. Current methods utilize text emb…

cs.IR2024

A Survey on Query-based API Recommendation

Moshi Wei, Nima Shiri Harzevili, Alvine Boaye Belle +6

Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing atte…

cs.SE2023★ 1 cited

Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge

Arunkaleeshwaran Narayanan, Nima Shiri harzevili, Junjie Wang +3

Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on de…

cs.SE2023★ 10 cited

The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

Jiho Shin, Moshi Wei, Junjie Wang +2

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature a…