# Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement

arXiv: https://arxiv.org/abs/2511.03421 · HTML: https://mlsanity.com/paper/2511.03421/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- Builds on 25 of its 65 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text Classification via Large Language Models](https://mlsanity.com/paper/2305.08377/) (2023)
- [How to estimate carbon footprint when training deep learning models? A guide and review](https://mlsanity.com/paper/2306.08323/) (2023)
- [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [Sampling Projects in GitHub for MSR Studies](https://mlsanity.com/paper/2103.04682/) (2021)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software](https://mlsanity.com/paper/1608.08933/) (2016)
- [Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services](https://mlsanity.com/paper/1608.05917/) (2016)
- [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Adapting Multi-objectivized Software Configuration Tuning](https://mlsanity.com/paper/2404.04744/) (2024)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025)
- [PromiseTune: Unveiling Causally Promising and Explainable Configuration Tuning](https://mlsanity.com/paper/2507.05995/) (2025)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [Are "Non-functional" Requirements really Non-functional?](https://mlsanity.com/paper/1611.08868/) (2016)
- [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025)
- [A Dataset for GitHub Repository Deduplication: Extended Description](https://mlsanity.com/paper/2002.02314/) (2020)
- [Self-Adaptation in Industry: A Survey](https://mlsanity.com/paper/2211.03116/) (2022)
- [Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification](https://mlsanity.com/paper/2509.13868/) (2025)
- [A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions](https://mlsanity.com/paper/2105.01011/) (2021)
- [An approach for performance requirements verification and test environments generation](https://mlsanity.com/paper/2403.00099/) (2024)

## Built on by (most cited first, 2 of 2)

- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems](https://mlsanity.com/paper/2605.08305/) (2026)

## Abstract (arXiv)

Elicited performance requirements need to be quantified for compliance in different engineering tasks, e.g., configuration tuning and performance testing. Much existing work has relied on manual quantification, which is expensive and error-prone due to the imprecision. In this paper, we present LQPR, a highly efficient automatic approach for performance requirements quantification.LQPR relies on a new theoretical framework that converts quantification as a classification problem. Despite the prevalent applications of Large Language Models (LLMs) for requirement analytics, LQPR takes a different perspective to address the classification: we observed that performance requirements can exhibit strong patterns and are often short/concise, therefore we design a lightweight linguistically induced matching mechanism. We compare LQPR against nine state-of-the-art learning-based approaches over diverse datasets, demonstrating that it is ranked as the sole best for 75% or more cases with two orders less cost. Our work proves that, at least for performance requirement quantification, specialized methods can be more suitable than the general LLM-driven approaches.