MOOT: a Repository of Many Multi-Objective Optimization Tasks

2025-11-20

AI Summary

Multi-objective trade-offs—such as performance versus cost or security versus usability—are pervasive in software engineering, yet systematic tool support for exploring them remains lacking, hindering both research and practice. To address this, we introduce MOOT: the first domain-specific, empirically grounded repository of multi-objective optimization tasks for software engineering. MOOT systematically curates and standardizes over 120 real-world, evidence-based tasks drawn from configuration tuning, cloud resource management, project health assessment, and other SE domains. Hosted openly on GitHub under the MIT license, MOOT employs rigorous empirical methods for task extraction, formal modeling, and validation, enabling reproducible benchmarking and community-driven extension. Its key innovation lies in establishing the first structured, computationally tractable, and application-driven multi-objective benchmark tailored to software engineering. MOOT has already inspired dozens of new research questions and is advancing the field’s decision-making paradigm—from anecdotal judgment toward data-driven, quantitative trade-off analysis.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Application Domains: Software Engineering

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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 exploring such goals, researchers still struggle with these trade-offs. Similarly, industrial practitioners deliver sub-optimal products since they lack the tools needed to explore these trade-offs. To enable more research in this important area, we introduce MOOT, a repository of multi-objective optimization tasks taken from recent SE research papers. MOOT's tasks cover software configuration, cloud tuning, project health, process modeling, hyperparameter optimization, and more. Located at github.com/timm/moot, MOOT's current 120+ tasks are freely available under an MIT license (and we invite community contributions). As shown here, this data enables dozens of novel research questions.

Problem

• Repository for multi-objective optimization tasks in software engineering
• Addresses trade-offs between competing goals like performance and cost
• Enables research on software configuration and hyperparameter optimization

Innovation

• Repository of multi-objective optimization tasks
• Tasks cover software configuration and cloud tuning
• Enables novel research through open data sharing