This paper introduces a framework that integrates reinforcement learning (RL) with autonomous agents to enable continuous improvement in the automated process of software test cases authoring from business requirement documents within Quality Engineering (QE) workflows. Conventional systems employing Large Language Models (LLMs) generate test cases from static knowledge bases, which fundamentally limits their capacity to enhance performance over time. Our proposed Reinforcement Infused Agentic RAG (Retrieve, Augment, Generate) framework overcomes this limitation by employing AI agents that learn from QE feedback, assessments, and defect discovery outcomes to automatically improve their test case generation strategies. The system combines specialized agents with a hybrid vector-graph knowledge base that stores and retrieves software testing knowledge. Through advanced RL algorithms, specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), these agents optimize their behavior based on QE-reported test effectiveness, defect detection rates, and workflow metrics. As QEs execute AI-generated test cases and provide feedback, the system learns from this expert guidance to improve future iterations. Experimental validation on enterprise Apple projects yielded substantive improvements: a 2.4% increase in test generation accuracy (from 94.8% to 97.2%), and a 10.8% improvement in defect detection rates. The framework establishes a continuous knowledge refinement loop driven by QE expertise, resulting in progressively superior test case quality that enhances, rather than replaces, human testing capabilities.

Abstract--The evolution of the 5S methodology with the support of artificial intelligence techniques represents a significant opportunity to improve industrial organization audits in the automotive chain, making them more objective, efficient and aligned with Industry 4.0 standards. This work developed an automated 5S audit system based on large-scale language models (LLM), capable of assessing the five senses (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in a standardized way through intelligent image analysis. The system's reliability was validated using Cohen's concordance coefficient (κ = 0.75), showing strong alignment between the automated assessments and the corresponding human audits. The results indicate that the proposed solution contributes significantly to continuous improvement in automotive manufacturing environments, speeding up the audit process by 50% of the traditional time and maintaining the consistency of the assessments, with a 99.8% reduction in operating costs compared to traditional manual audits. The methodology presented establishes a new paradigm for integrating lean systems with emerging AI technologies, offering scalability for implementation in automotive plants of different sizes. The global automotive industry faces growing competitiveness challenges demanding maximized operational efficiency and production quality. The 5S methodology, recognized worldwide as the foundation for workplace organization and cleanliness, plays a strategic role in operational excellence.

Despite the indisputable benefits of Continuous Integration (CI) pipelines (or builds), CI still presents significant challenges regarding long durations, failures, and flakiness. Prior studies addressed CI challenges in isolation, yet these issues are interrelated and require a holistic approach for effective optimization. To bridge this gap, this paper proposes a novel idea of developing Digital Twins (DTs) of build processes to enable global and continuous improvement. To support such an idea, we introduce the CI Build process Digital Twin (CBDT) framework as a minimum viable product. This framework offers digital shadowing functionalities, including real-time build data acquisition and continuous monitoring of build process performance metrics. Furthermore, we discuss guidelines and challenges in the practical implementation of CBDTs, including (1) modeling different aspects of the build process using Machine Learning, (2) exploring what-if scenarios based on historical patterns, and (3) implementing prescriptive services such as automated failure and performance repair to continuously improve build processes.

Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints on efficiency, safety, and overall training stability, which limits its practical applicability. We present APRL, a policy regularization framework that modulates the robot's exploration over the course of training, striking a balance between flexible improvement potential and focused, efficient exploration. APRL enables a quadrupedal robot to efficiently learn to walk entirely in the real world within minutes and continue to improve with more training where prior work saturates in performance. We demonstrate that continued training with APRL results in a policy that is substantially more capable of navigating challenging situations and is able to adapt to changes in dynamics with continued training.

Role requiring'No experience data provided' months of experience in Chicago A Series B SaaS company in the cyber security realm is looking for a Senior Java Developer to join a fast paced agile team in their Mclean office, although this role would offer partial remote. The ideal candidate will be brought in to expand a highly scalable fault tolerant cloud service, working with micro-service architecture, auto scaling, and application development. Qualified candidates will have experience with J2ee technologies, developed web services from scratch, interfaced external API's with Rest, understanding of spring frameworks, experience with AWS and exposure to CI/CD with Jenkins. If you are interested in join an industry favorite start-up please apply. Applicants must be currently authorized to work in the United States on a full-time basis now and in the future.

The following is sponsored content. Achieving LFPSE (Learning from Patient Safety Events) compliance is more than just meeting targets – the real driver is transforming patient safety by enabling continuous improvement, says Mark Fewster, chief product officer with Radar Healthcare. The way that health care workers report on patient safety events is changing – and the deadline for making it happen is looming. By March 2023, healthcare organisations in England should have transitioned from the current NRLS (National Reporting and Learning System) and be LFPSE (Learning from Patient Safety Events) compliant. This is more than a change in initials – the new system aims to transform how patient safety events are recorded across the country.

Owners of the popular Nintendo Switch games console have been left footing hefty bills to replace faulty Joy-Con controllers because of a flaw in the technology, according to a new report. Since the Nintendo Switch was launched in 2017, there have been many reports of'Joy-Con drift', where the console registers movement even when players are not touching the controllers. Now a survey by Which? has revealed that one in five Nintendo Switch Classic owners in the UK have experienced the problem. The consumer watchdog is calling on Nintendo to commission an independent investigation into the issue, and is urging the gaming giant to make the findings of any investigation public. Nintendo says the issue has only affected a small number of devices.

As COVID-19 began surging across the globe in the early months of 2020, it almost immediately flooded health care providers with challenges and demands the industry had never before seen. Suddenly doctors' offices were closed to patients, hospital emergency departments and ICUs were running out of beds, and healthcare workers were battling a new and unknown disease. Today, in the waning months of 2021, some of these pressures have eased, while others keep coming back along with the Delta variant. Either way, the insights they produced remain vitally important. Lessons learned during this stressful time have already inspired fundamental changes in healthcare--changes that are here to stay.

At Sofbang, chatbots and artificial intelligence are two of our key areas of focus. As more and more businesses leverage chatbot technology and machine learning to streamline operations during this tough economic time, we're constantly getting the question, "How does retraining the bot to make it better over time work?" We're here to answer that question in a very specific way. A center of excellence (COE) is a framework for continuous improvement that involves a dedicated team, a repeatable, data-minded process, and the right technology to enable agile changes and provide strong orchestration. A COE team develops, refines, and strengthens their core area of focus through data study, applying emerging best practices, and communication with employees and clients about evolving needs.

Every organization has had to react to COVID-19 impacts. Before the pandemic hit, organizations were executing digital transformation strategies that were created in response to digital disruption. Then, in 2020, the pandemic hit making digital disruption seem comparatively tame. For one thing, digital disruption takes a couple or a few years whereas pandemic emergency response needed to be executed within days or weeks. Now digital transformation has evolved into yet another stage. This third stage combines the thought processes of the previous two phases.