QuALITY
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Related Articles from SNS
AI-Augmented Closed-Loop Quality Engineering: A Reference Architecture for Continuous Software Quality Intelligence
Announce Type: new Abstract: The quality of software engineering is still under a challenge due to disjointed processes between requirements, testing, and production, which hinders the opportunity to implement quality strategies in consecutive releases. Existing approaches tend to be fixed-model or single-optimization approaches and lack production feedback learning mechanisms. The paper at hand proposes a closed-loop reference architecture of continuous software quality intelligence with AI...
HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment
arXiv:2508.15130v3 Announce Type: replace Abstract: Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder their generalization performance. We propose HiRQA (Hierarchical Ranking and Quality Alignment), a self-supervised, opinion-unaware framework that offers a hierarchical, quality-aware embedding through a combination of ranking and contrastive learning. Unlike prior approaches that depend on...
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
arXiv:2606.03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs) have emerged as a promising paradigm for TS quality assessment via pairwise comparison and per-dimension evaluation. However, existing approaches rely on manually predefined quality dimensions and purely text-based reasoning, leaving it unknown whether LLMs can...
QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation
Announce Type: replace Abstract: Automating requirements quality analysis remains challenging because multiple, often conflicting quality attributes must be balanced while preserving stakeholder intent. Existing Large-Language-Model (LLM) approaches predominantly rely on task-oriented decomposition or implicit aggregation, limiting their ability to systematically surface and resolve cross-quality conflicts. We present QUARE (QUality-Aware REquirements Analysis), a multi-agent framework that...
Quality report: taxpayers and traders
Quality report: taxpayers and traders Quality report for statistics related to numbers of taxpayers and registered traders. This methodology and quality report relates to the Official Statistic publication, numbers of taxpayers and registered traders, and the purpose is to provide users with background information on the methodology and the quality of outputs such as data suitability and coverage. Updates to this page - This report has been updated to include the latest available data for...
Quality report: Alcohol Bulletin
A quality report on the Alcohol Bulletin which contains quarterly statistics from the 4 different alcohol duty regimes administered by HM Revenue and Customs. This quality report relates to the Alcohol Bulletin Accredited Official Statistics. The purpose is to provide users with information about the quality of the outputs as set out by the Code of Practice for Official Statistics.
High-Quality Entity Segmentation and Grounding
arXiv:2402.02555v2 Announce Type: replace Abstract: In this work, we propose ESG, a pipeline for high-quality entity segmentation and grounding supported by a new dataset EntitySeg. At first, the proposed dataset naming EntitySeg contains images spanning various image domains and entities, along with plentiful high-resolution images and high-quality mask annotations for training and testing. Then, the ESG mainly consists of two modules: CropFormer for high-quality entity segmentation whereas...
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
arXiv:2506.11066v3 Announce Type: replace Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency,...
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
arXiv:2511.05650v2 Announce Type: replace Abstract: Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended generation tasks. We propose Base-Aligned Model Collaboration (BACo), an inference-time token-level model collaboration framework that dynamically combines a base LLM with its aligned counterpart to optimize diversity and quality. Using uncertainty and...
RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision
arXiv:2606.06176v1 Announce Type: new Abstract: Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels.