5 papers
Optimization-Aware Test Generation for Deep Learning Compilers
Qingchao Shen, Zan Wang, Haoyang Ma +5
Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring…
Testing Question Answering Software with Context-Driven Question Generation
Shuang Liu, Zhirun Zhang, Jinhao Dong +5
Question-answering software is becoming increasingly integrated into our daily lives, with prominent examples including Apple Siri and Amazon Alexa. Ensuring the quality of such sy…
Optimizing Knowledge Utilization for Multi-Intent Comment Generation with Large Language Models
Shuochuan Li, Zan Wang, Xiaoning Du +3
Code comment generation aims to produce a generic overview of a code snippet, helping developers understand and maintain code. However, generic summaries alone are insufficient to…
RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
Zhuo Wu, Qinglin Jia, Chuhan Wu +4
Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm e…
A Tale of Two DL Cities: When Library Tests Meet Compiler
Qingchao Shen, Yongqiang Tian, Haoyang Ma +5
Deep Learning (DL) compilers typically load a DL model and optimize it with intermediate representation.Existing DL compiler testing techniques mainly focus on model optimization s…