MOOT: a Repository of Many Multi-Objective Optimization Tasks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918326010118144 |
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| author | Menzies, Tim Chen, Tao Ye, Yulong Ganguly, Kishan Kumar Rayegan, Amirali Srinivasan, Srinath Lustosa, Andre |
| author_facet | Menzies, Tim Chen, Tao Ye, Yulong Ganguly, Kishan Kumar Rayegan, Amirali Srinivasan, Srinath Lustosa, Andre |
| contents | 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 tasks in SE. We note that MOOT's novelty is infrastructural, not algorithmic-we contribute curated data and research enablement, not new optimization methods. MOOT enables harder and more credible research. MOOT lets us replace studies on toy problems (or just half a dozen hand-picked examples) with case studies on 120+ examples. Such studies could focus on stability, sample efficiency, failure modes, cross-domain generality, or many other questions (see list in this document). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_16882 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MOOT: a Repository of Many Multi-Objective Optimization Tasks Menzies, Tim Chen, Tao Ye, Yulong Ganguly, Kishan Kumar Rayegan, Amirali Srinivasan, Srinath Lustosa, Andre Software Engineering 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 tasks in SE. We note that MOOT's novelty is infrastructural, not algorithmic-we contribute curated data and research enablement, not new optimization methods. MOOT enables harder and more credible research. MOOT lets us replace studies on toy problems (or just half a dozen hand-picked examples) with case studies on 120+ examples. Such studies could focus on stability, sample efficiency, failure modes, cross-domain generality, or many other questions (see list in this document). |
| title | MOOT: a Repository of Many Multi-Objective Optimization Tasks |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.16882 |