arXiv cs.AI· Chengjie Chuan, Pengzhou Chen, Tao Chen·· 10 小时前AI 评分18
将 Meta Multi-Objectivization 专门化为软件工程优化的基线方法
Specializing Meta Multi-Objectivization as Baseline for Software Engineering Optimization
AI 导读
研究将 Meta Multi-Objectivization（MMO）专门化为跨软件工程优化问题的通用基线，在涵盖 212 个案例的 9 类 SE 优化问题上，选对辅助目标时 MMO 分别以 79% 和 94% 的比例优于其他 SOTA 与通用优化器。作者据此提出工具 L2RAC，可为给定 SE 问题自动推荐最佳辅助目标选择，排序准确率最高提升 60%，并节省多达 99% 的试错成本。
正文 · 原文
Abstract:Given the discrete nature of many software engineering (SE) optimization problems, trapping at local optima remains a crucial issue in the optimizer designs. Meta Multi-Objectivization (MMO) is an optimization model for overcoming local optima by introducing an extra, auxiliary objective. Yet, MMO is originally designed only for configuration tuning, leaving its suitability for other SE problems in doubt, particularly regarding the choice of the auxiliary objective. In this paper, we study the possibility and resolution for specializing MMO as a general baseline across a range of SE optimization problems, making it a fundamental counterpart for future SE optimizers that seek to mitigate the issue of local optima. We do so by providing both theoretical remit and empirical evidence: we analytically show how the choice of the auxiliary objective is crucial while demonstrating the effectiveness of MMO under nine SE optimization problems comprising 212 cases when the right one can be chosen, i.e., MMO is able to outperform the other state-of-the-art and general-purpose optimizers in 79% and 94% of the cases, respectively. Drawing on the theoretical understanding and empirical findings, we then propose L2RAC, a tool that automatically recommends the best auxiliary objective choice for a given SE problem/case, hence helping to specialize MMO therein. Through extensive experiments, we reveal that, in general, L2RAC can more accurately rank the choice of auxiliary objective for MMO under a case than other guesses by up to 60%, saving as much as 99% of the cost that would otherwise be required to conduct trial-and-error when using MMO.
| Comments: | Accepted by IEEE Transactions on Software Engineering (TSE) |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.12991 [cs.SE] |
| (or arXiv:2611.12991v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12991 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chengjie Chuan [view email]
[v1]
Sun, 8 Nov 2026 10:43:08 UTC (4,638 KB)
来源：arXiv cs.AI · arxiv.org