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human-in-the-loop online just-in-time software defect prediction: What have we achieved and what do we still miss?

SCIENCE OF COMPUTER PROGRAMMING 2025年 244卷

作者： Liu, Xutong Zhou, Yufei Tang, Yutian Qian, Junyan Zhou, Yuming Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Guangxi Peoples R China Univ Glasgow Sch Comp Sci Glasgow G12 8QN Scotland

Background. The Online Just-In-Time Software Defect Prediction (O-JIT-SDP) employs an online model to predict whether a new software change will introduce a bug. Previous studies have neglected to consider the interaction between Software Quality Assurance (SQA) personnel and the model, potentially missing opportunities to refine prediction accuracy through human feedback. Problem. A recent study introduced the first human-in-the-loop (HITL) O-JIT-SDP framework called HumLa, integrating SQA staff feedback without accounting for inspection time to boost the prediction performance of O-JIT-SDP. However, upon a thorough revisit of HumLa, we find that while certain aspects of the HITL O-JIT-SDP system appear feasible in ideal conditions, they prove impractical in real-world context. Objective. We aim to reformulate HITL O-JITSDP, which are crucial yet absent for practical application. Method. We propose four crucial enhancements to facilitate practical application of HITL O-JIT-SDP. First, we advocate for the use of observed labels rather than ground-truth labels to evaluate online classifiers in real-world settings. Second, we suggest refraining from utilizing the entire data stream for normalizing features of each new instance, as was done in HumLa. Third, we propose incorporating nonzero SQA inspection time into the formulation of HITL O-JIT-SDP. Fourth, we introduce realtime statistical classifier comparison into the HITL system. Result. Our replication uncovers that the performance evaluation of HumLa under a practical scenario significantly deviate from the originally reported performance under an ideal experimental scenario, potentially diminishing the promise of HITL O-JIT-SDP. Furthermore, with our enhanced HITL O-JIT-SDP framework, we revisit a fundamental question in O-JIT-SDP: the benefits of HITL integration. Our experimental findings demonstrate that HITL not only enhances O-JIT-SDP when SQA feedback surpasses Bug- Fixing Commit (BFC) feedback (by providin

关键词： Software change Defect prediction human-in-the-loop

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human-in-the-loop Myoelectric Pattern Recognition Control of an Arm-Support Robot to Improve Reaching in Stroke Survivors

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1108-1117页

作者： Kopke, Joseph V. Ellis, Michael D. Hargrove, Levi J. Northwestern Univ Dept Biomed Engn Chicago IL 60611 USA JBVAMC Vet Affairs Off Res & Dev Chicago IL 60612 USA Northwestern Univ Feinberg Sch Med Dept Phys Therapy & Human Movement Sci Chicago IL 60611 USA Northwestern Univ Feinberg Sch Med Dept Phys Med & Rehabil Chicago IL 60611 USA Shirley Ryan AbilityLab Ctr Bion Med Chicago IL 60611 USA Northwestern Univ Dept Biomed Engn Chicago IL 60611 USA Northwestern Univ Dept Phys Med & Rehabil Chicago IL 60611 USA

The objective of this study was to assess the feasibility and efficacy of using real-time human-in-the-loop pattern recognition-based myoelectric control to control vertical support force or vertical position to improve reach in individuals with chronic stroke. This work attempts to move proven lab-based static arm support paradigms towards a controllable wearable device. A machine learning (linear discriminant analysis)-based myoelectric pattern recognition system based on movement intent as determined by real-time muscle activation was used to control incremental changes in either vertical position or vertical support force during a reach and retrieve task, with the goal of improving reaching function. Performance under real-time control of both options was compared to two unchanging static-support conditions (current gold standard) and a no-support condition. Both real-time control paradigms were successfully implemented and resulted in greater forward-reaching performance as demonstrated by increased elbow extension and horizontal shoulder adduction compared to no-support and was not different from the current gold standard static support paradigms. Muscle activation levels with real-time support were lower than the no-support condition and similar to those observed during the static support paradigms. Real-time detection of user intent was successful in controlling both vertical position and vertical support force and enabled greater reaching distance than without it demonstrating both its feasibility and efficacy albeit with some limitations.

关键词： Shoulder Real-time systems Robots Muscles Electromyography Pattern recognition Elbow Myoelectric control Gravity End effectors Rehabilitation robotics human-in-the-loop stroke rehabilitation myoelectric control pattern recognition

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human-in-the-loop Robot Learning for Smart Manufacturing: A human-Centric Perspective

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11062-11086页

作者： Chen, Hongpeng Li, Shufei Fan, Junming Duan, Anqing Yang, Chenguang Navarro-Alarcon, David Zheng, Pai Hong Kong Polytech Univ Dept Ind & Syst Engn SAR Hong Kong Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Robot Dept Abu Dhabi U Arab Emirates Univ Liverpool Dept Comp Sci Liverpool L69 3BX England Hong Kong Polytech Univ Dept Mech Engn Hong Kong Peoples R China

Robot learning has attracted an ever-increasing attention by automating complex tasks, reducing errors, and increasing production speed and flexibility, which leads to significant advancements in manufacturing intelligence. However, its low training efficiency, limited real-time feedback, and challenges in adapting to untrained scenarios hinder its applications in smart manufacturing. Introducing a human role in the training loop, a practice known as human-in-the-loop (HITL) robot learning, can improve the performance of robots by leveraging human prior knowledge. Nonetheless, the exploration of HITL robot learning within the context of human-centric smart manufacturing remains in its infancy. This study provides a holistic literature review for understanding HITL robot learning within an industrial context from a human-centric perspective. A united structure is presented to encompass different aspects of human intelligence in HITL robot learning, highlighting perception, cognition, behavior, and notably, empathy. Then, the typical applications in manufacturing scenarios are analyzed to expand the research landscape for smart manufacturing. Finally, it introduces the empirical challenges and future directions for HITL robot learning in the next industrial revolution era. Note to Practitioners-This review is motivated by the emergence of the next generation of smart manufacturing, which emphasizes the coexistence of humans and robotics in the manufacturing workstation to mitigate inherent limitations of each. It presents an overview of HITL robot learning-related works to identify state-of-the-art and significant focuses for human-centric smart manufacturing. It classifies representative studies into detailed sub-categories based on various facets of human intelligence, highlighting perception, cognition, behavior, and empathy, providing a complete and detailed survey of this field. The applications in manufacturing scenarios are analyzed, and we discuss the possible

关键词： Robots Robot learning Smart manufacturing Service robots Automation Training human intelligence Cognition Production Robot sensing systems smart manufacturing human-in-the-loop human guidance

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human-in-the-loop transfer learning in collision avoidance of autonomous robots

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Biomimetic Intelligence & Robotics 2025年第1期5卷 124-137页

作者： Minako Oriyama Pitoyo Hartono Hideyuki Sawada Department of Pure and Applied Physics Graduate School of Advanced Science and EngineeringWaseda UniversityTokyo 169-8555Japan School of Engineering Chukyo UniversityAichi 466-8666Japan Department of Applied Physics Faculty of Science and EngineeringWaseda UniversityTokyo 169-8555Japan

Neural networks have demonstrated exceptional performance across a range of ***,their training often demands substantial time and data resources,presenting a challenge for autonomous robots operating in real-world environments where real-time learning is *** mitigate this constraint,we propose a novel human-in-the-loop framework that harnesses human expertise to mitigate the learning challenges of autonomous *** approach centers on directly incorporating human knowledge and insights into the robot’s learning *** proposed framework incorporates a mechanism for autonomous learning from the environment via reinforcement learning,utilizing a pre-trained model that encapsulates human knowledge as its *** integrating human-provided knowledge and evaluation,we aim to bridge the division between human intuition and machine learning *** a series of collision avoidance experiments,we validated that incorporating human knowledge significantly improves both learning efficiency and generalization *** collaborative learning paradigm enables robots to utilize human common sense and domain-specific expertise,resulting in faster convergence and better performance in complex *** research contributes to the development of more efficient and adaptable autonomous robots and seeks to analyze how humans can effectively participate in robot learning and the effects of such participation,illuminating the intricate interplay between human cognition and artificial intelligence.

关键词： human-in-the-loop Transfer learning Autonomous robots Neural networks Reinforcement learning

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Adaptive Task Assignment in Spatial Crowdsourcing: A human-in-the-loop Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2726-2739页

作者： Wu, Qingshun Li, Yafei Yan, Jinxing Zhang, Mei Xu, Jianliang Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called human-in-the-loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.

关键词： human in the loop Crowdsourcing Roads Reinforcement learning Mobile computing Learning systems Partitioning algorithms Costs Symbols Real-time systems human-in-the-loop location-based services reinforcement learning spatial crowdsourcing task assignment

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Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization

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KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Guay-Hottin, Rose Kardassevitch, Lison Pham, Hugo Lajoie, Guillaume Bonizzato, Marco Polytech Montreal Elect Engn Dept Montreal PQ H3T 1J4 Canada MILA Quebec Inst Montreal PQ H2S 3H1 Canada Univ Montreal Neurosci Dept Montreal PQ H3T 1J4 Canada Univ Montreal Ctr interdisciplinaire Rech Cerveau & Apprentissag Montreal PQ H3T 1J4 Canada Univ Montreal Math & Stat Dept Montreal PQ H3T 1J4 Canada

In diverse fields of application, Bayesian Optimization (BO) has been proposed to find the optimum of black- box functions, surpassing human-driven searches. BO's appeal lies in its data efficiency, making it suitable for optimizing costly-to-evaluate functions without requiring extensive training data. While BO can perform well in closed-loop, domain experts frequently have hypotheses about which parameter combinations are more likely to yield optimal results. Hence, for BO to be truly relevant and adopted by practitioners, such prior knowledge needs to be efficiently and seamlessly integrated into the optimization framework. Some methods were recently developed to address this challenge, but they suffer from robustness issues when provided erroneous insight. Building on the idea of element-wise prior-weighted acquisition function, we propose to use a fixed-weight effective prior that distills expert user knowledge with minimal computational cost. Comprehensive investigation across diverse task conditions and prior quality levels revealed that our method, alpha- t BO, surpasses Vanilla BO when provided with insights of good quality while maintaining robustness against misleading information. Moreover, unlike other methods, alpha- t BO typically requires no hyperparameter tuning, largely simplifying its implementation in diverse tasks.

关键词： Bayesian optimization Domain knowledge integration Prior-weighted acquisition function Region of interest human-in-the-loop

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Automated human-in-the-loop commissioning of industrial processes: a glue dispensing case study

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期136卷 4991-5005页

作者： Taets, Jeroen Jordens, Jeroen Lefebvre, Tom Ostyn, Frederik Crevecoeur, Guillaume Univ Ghent Electromech Syst & Met Engn Technol Pk Zwijnaarde 131 B-9052 Ghent Belgium Flanders Make Core Lab MIRO Lommel Belgium Flanders Make CodesignS Oude Diestersebaan 133 B-3920 Lommel Belgium

This paper addresses the challenge of tuning process parameters during the commissioning of industrial processes. The process industry is hindered by unique challenges, including the absence of physical models and weakly specified bounds on process parameters. Moreover, multiple experiments can be performed simultaneously in a batch, but only under some extra constraints, such as a constant temperature for every experiment in the batch. These observations motivate the need for practical commissioning tools that can assist human machine operators or technicians. While there are solutions available for automatic tuning, they often lack the simplicity required for an operator-in-the-loop to easily understand and fine-tune the automatic tuning without being a machine learning expert. Therefore, we propose a simple Bayesian optimisation (BO) method with a single hyper-parameter and a systematic approach that can be easily understood. The effectiveness of the algorithm is demonstrated for glue dispensing, a complex industrial process with diverse properties that make tuning cumbersome. We validated our method in three cases, reducing the commissioning time by an inexperienced human-in-the-loop from days to hours.

关键词： Artificial intelligence Batch Bayesian optimisation Glue bead process human-in-the-loop Process industry

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A novel classification method based on an online extended belief rule base with a human-in-the-loop strategy

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APPLIED INTELLIGENCE 2025年第7期55卷 1-26页

作者： Li, Jinyuan Qian, Guangyu He, Wei Zhu, Hailong Zhou, Guohui Harbin Normal Univ Sch Comp Sci & Informat Engn Harbin 150025 Peoples R China

Classification methods, such as fault diagnosis and intrusion detection, are widely used in modeling complex systems. The accuracy and credibility of these methods directly affect the reliability of the modeling results, which in turn determines the effectiveness of engineering decisions. Additionally, the model's ability to be dynamically updated should be considered, given the intricate and ever-changing nature of engineering environments. For online models, adding new training samples without considering their suitability can lead to problems such as poor model performance and increased rule base complexity. Furthermore, amid constantly arriving new samples in a dynamic environment, modeling based only on initial expert knowledge can result in new samples not being fully used. Therefore, a novel classification method based on an online extended belief rule base with a human-in-the-loop strategy (OEBRB-H) is proposed in this paper. First, a fuzzy c-means algorithm based on expert knowledge (FBE) is designed to evaluate model parameters online. Second, a human-in-the-loop strategy for dividing the new sample set and a domain-value-based rule updating method are proposed for model optimization. Finally, two case studies, namely, aeroengine inter-shaft bearing fault diagnosis and industrial control intrusion detection, are performed. The results indicate that the model proposed in this paper can maintain both credibility and high accuracy in dynamic environments.

关键词： Classification method Extended belief rule base Online updating human-in-the-loop Online evaluation of parameters

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LLM-TSFD: An industrial time series human-in-the-loop fault diagnosis method based on a large language model

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Zhang, Qi Xu, Chao Li, Jie Sun, Yicheng Bao, Jinsong Zhang, Dan Donghua Univ Coll Mech Engn Shanghai 201602 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong Peoples R China

Industrial time series data provides real-time information about the operational status of equipment and helps identify anomalies. Data-driven and knowledge-guided methods have become predominant in this field. However, these methods depend on industrial domain knowledge and high-quality industrial data which can lead to issues such as unclear diagnostic results and lengthy development cycles. This paper introduces a novel humanin-the-loop task-driven approach to reduce reliance on manually annotated data and improve the interpretability of diagnostic outcomes. This approach utilises a large language model for fault detection, fostering process autonomy and enhancing human-machine collaboration. Furthermore, this paper explores four key roles of the large language model: managing the data pipeline, correcting causality, controlling model management, and making decisions about diagnostic results. Additionally, it presents a prompt structure designed for fault diagnosis of time series data, enabling the large language model to realize task-driven. Finally, the paper validates the proposed framework through a case study in the context of steel metallurgy.

关键词： Time series Fault diagnosis 2.0 Task-driven Large language model human-in-the-loop

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Cyber-physical systems with human-in-the-loop: A systematic review of socio-technical perspectives

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 226卷

作者： Clemmensen, Torkil Moghaddam, Mahyar Tourchi Norbjerg, Jacob Copenhagen Business Sch Dept Digitalizat Howitzvej 60 DK-2000 Frederiksberg Denmark Univ Southern Denmark Maersk Mc Kinney Moller Inst SDU Software Engn Campusvej 55 DK-5230 Odense Denmark

Understanding and designing Cyber-physical systems (CPS) with humans in the loop (HITL) is a basic crossscientific research problem with large implications for industry. The current software engineering knowledge already explains how to include the humans in the operation of the machines in terms of interfaces, architectures, adaptive systems, and design methodologies for including the human-in-the-loop. This paper extends existing knowledge with a systematic review of socio-technical perspectives on CPS with HITL. The review was software engineering focused, as it searched the body of research on CPS with HITL, and only within that body, those papers that included socio-technical perspectives. The results indicated four main areas in the ST literature. Validating these insights by expert interviews with industry CPS experts showed some alignment and also fundamental differences between the socio-technical literature (ST literature) insights and the industry experts' viewpoints. The discussion identifies useful crossings between the ST literature and research into CPS with HITL adaption, and touch on the issues of non-alignments in industry practice. The conclusion is that the ST perspectives on the body of knowledge on CPS with HITL has much to offer researchers in terms of innovative ways to look at the HITL, but the literature needs further development before industry experts can effectively use it. Future research possibilities are outlined.

关键词： Cyber-physical systems human-in-the-loop Socio-technical

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