MOOT: a Repository of Many Multi-Objective Optimization Tasks
Tim Menzies, Tao Chen, Yulong Ye, Kishan Kumar Ganguly, Amirali Rayegan, Srinath Srinivasan, Andre Lustosa

TL;DR
MOOT is a comprehensive repository of over 120 real-world multi-objective optimization tasks in software engineering, designed to facilitate more credible and diverse research by replacing toy problems with extensive case studies.
Contribution
It provides the largest curated collection of multi-objective optimization tasks in SE, enabling more realistic and varied research without introducing new algorithms.
Findings
Largest collection of SE multi-objective tasks
Enables more credible and diverse research
Supports case studies on stability and generality
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 address this, MOOT (http://tiny.cc/moot) is a repository of many SE multi-objective optimization tasks. MOOT's 120+ tasks cover software configuration, cloud tuning, project health, process modeling, hyperparameter optimization, and more. Sample scripts for reading MOOT and generating baseline results are available -- just clone the repository and run the sample rqx.sh files (from tiny.cc/moot0). To the best of our knowledge, MOOT is the largest and most varied collection of real multi-objective optimization…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Process Optimization and Integration · Advanced Software Engineering Methodologies
