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cs.SE2026

Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation

Shihai Wang, Tao Chen

Since software performance requirements are documented in natural language, quantifying them into mathematical forms is essential for software engineering. Yet, the vagueness in pe…

cs.SE2026

MOOT: a Repository of Many Multi-Objective Optimization Tasks

Tim Menzies, Tao Chen, Yulong Ye +4

Software engineers must make decisions that trade off competing goals (faster vs. cheaper, secure vs. usable, accurate vs. interpretable, etc.). Despite MSR's proven techniques for…

cs.SE2025

Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement

Shihai Wang, Tao Chen

Elicited performance requirements need to be quantified for compliance in different engineering tasks, e.g., configuration tuning and performance testing. Much existing work has re…

cs.SE2025

CoTune: Co-evolutionary Configuration Tuning

Gangda Xiong, Tao Chen

To automatically tune configurations for the best possible system performance (e.g., runtime or throughput), much work has been focused on designing intelligent heuristics in a tun…

cs.SE2025

The Same Only Different: On Information Modality for Configuration Performance Analysis

Hongyuan Liang, Yue Huang, Tao Chen

Configuration in software systems helps to ensure efficient operation and meet diverse user needs. Yet, some, if not all, configuration options have profound implications for the s…

cs.SE2025

Distilled Lifelong Self-Adaptation for Configurable Systems

Yulong Ye, Tao Chen, Miqing Li

Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the conf…