arXiv AI June 9, 2026 By Dimple Bajaj

AI-Augmented Closed-Loop Quality Engineering: A Reference Architecture for Continuous Software Quality Intelligence

arXiv:2606. 08793v1 Announce Type: cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

1d ago

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).

By David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms benchmarks

arXiv Machine Learning

Jun 25

Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues

arXiv:2606. 24968v1 Announce Type: new Abstract: Context: Software defect prediction supports maintenance decisions such as testing prioritization, release-risk assessment, and quality monitoring.

By Emmanuel Charleson Dapaah, Philip Makedonski, Jens Grabowski

data-engineering

arXiv AI

Aug 3

RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment

arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.

By Marcos Fuster-Pena, David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms reinforcement-learning benchmarks

arXiv AI

Jun 30

Data and Evaluation Closed-Loop for Model Capability Enhancement

arXiv:2606. 28471v1 Announce Type: new Abstract: Model capability is the central variable in LLM pre-training, yet is never observed directly: data shapes it prospectively, while evaluation reveals it only retrospectively, compressing samples, prompts, decoding, and scoring rules into one noisy score.

By Zhixuan Li, Jiangan Yuan, Han Xu

llms benchmarks

arXiv AI

Jul 21

Automated Hardware Validation Test Plan Generation for Large Scale AI Datacenter Platforms Using a Generative AI Multi-Agents Architecture

arXiv:2607. 16388v1 Announce Type: cross Abstract: Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans.

By Mohammed-Khalil Ghali, Saurabh Kulkarni, Prathamesh Kulkarni, Rohan Kulkarni, Sangwon Yoon, Daehan Won

agents

arXiv Machine Learning

Jul 30

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage

arXiv:2604. 01527v4 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals.

By Smriti Jha, Matteo Paltenghi, Chandra Maddila, Vijayaraghavan Murali, Shubham Ugare, Satish Chandra

llms agents benchmarks

Related stories

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues

RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment

Data and Evaluation Closed-Loop for Model Capability Enhancement

Automated Hardware Validation Test Plan Generation for Large Scale AI Datacenter Platforms Using a Generative AI Multi-Agents Architecture

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage