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1. Simultaneous fault prediction in evolving industrial environments with ensembles of Hoeffding adaptive trees

   https://doi.org/10.1007/s10489-025-06786-7  

   Esteban, A; Cano, A; Ventura, S; Zafra, A (August 2025, Applied Intelligence)

   Abstract Predictive Maintenance (PdM) emerges as a critical task of Industry 4.0, driving operational efficiency, minimizing downtime, and reducing maintenance costs. However, real-world industrial environments present unsolved challenges, especially in predicting simultaneous and correlated faults under evolving conditions. Traditional batch-based and deep learning approaches for simultaneous fault prediction often fall short due to their assumptions of static data distributions and high computational demands, making them unsuitable for dynamic, resource-constrained systems. In response, we propose OEMLHAT (Online Ensemble of Multi-Label Hoeffding Adaptive Trees), a novel model tailored for real-time, multi-label fault prediction in non-stationary industrial settings. OEMLHAT introduces a scalable online ensemble architecture that integrates online bagging, dynamic feature subspacing, and adaptive output weighting. This design allows it to efficiently handle concept drift, high-dimensional input spaces, and label sparsity, key bottlenecks in existing PdM solutions. Experimental results on three public multi-label PdM case studies demonstrate substantial improvements in predictive performance of OEMLHAT over previous batch-based and online proposals for multi-label classification, particularly with an average improvement in micro-averaged F1-score of 18.49% over the second most-accurate batch-based proposal and of 8.56% in the case of the second best online model. By addressing a critical gap in online multi-label learning for PdM, this work provides a robust and interpretable solution for next-generation industrial monitoring systems for fault detection, particularly for rare and concurrent failures. 

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2. Generating Effective Test Suites by Combining Coverage Criteria

   https://doi.org/10.1007/978-3-319-66299-2_5  

   Gay, Gregory (August 2017, International Symposium on Search Based Software Engineering)

   A number of criteria have been proposed to judge test suite adequacy. While search-based test generation has improved greatly at criteria coverage, the produced suites are still often ineffective at detecting faults. Efficacy may be limited by the single-minded application of one criterion at a time when generating suites - a sharp contrast to human testers, who simultaneously explore multiple testing strategies. We hypothesize that automated generation can be improved by selecting and simultaneously exploring multiple criteria. To address this hypothesis, we have generated multi-criteria test suites, measuring efficacy against the Defects4J fault database. We have found that multi-criteria suites can be up to 31.15% more effective at detecting complex, real-world faults than suites generated to satisfy a single criterion and 70.17% more effective than the default combination of all eight criteria. Given a fixed search budget, we recommend pairing a criterion focused on structural exploration - such as Branch Coverage - with targeted supplemental strategies aimed at the type of faults expected from the system under test. Our findings offer lessons to consider when selecting such combinations. 

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3. A Survey of the Application of Combinatorial Testing

   https://doi.org/10.1109/QRS-C.2019.00100  

   Li, Zhao; Chen, Yuhang; Gong, Guoqiang; Li, Dongcheng; Lv, Ke; Chen, Peng (July 2019, The 19th IEEE International Conference on Software Quality, Reliability and Security Companion)

   After more than 30 years of development, combinatorial testing has become an essential method in the field of software testing, which has always been an active field. Through combined coverage, combinatorial testing can detect faults caused by various parameters and their interactions in a software under test. This paper aims to review the development of combinatorial testing and briefly introduce its basic applications. We classify today's combinatorial testing applications, including those of traditional industry and those of the IT industry. The research-and-application progress of combinatorial testing was investigated with respect to various fields of application, and potential application directions for the future were proposed to provide ideas its the extensive application. 

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4. Metamorphic Testing: A Simple yet Effective Approach for Testing Scientific Software

   https://doi.org/10.1109/MCSE.2018.2875368  

   Kanewala, Upulee; Chen, Tsong Yueh (October 2018, Computing in Science & Engineering)

   Testing scientific software is a difficult task due to their inherent complexity and the lack of test oracles. In addition, these software systems are usually developed by end user developers who are neither normally trained as professional software developers nor testers. These factors often lead to inadequate testing. Metamorphic testing is a simple yet effective testing technique for testing such applications. Even though MT is a well-known technique in the software testing community, it is not very well utilized by the scientific software developers. The objective of this article is to present MT as an effective technique for testing scientific software. To this end, we discuss why MT is an appropriate testing technique for scientists and engineers who are not primarily trained as software developers. Especially, how it can be used to conduct systematic and effective testing on programs that do not have test oracles without requiring additional testing tools. 

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5. A Systematic Review of Sensor Vulnerabilities and Cyber‐Physical Threats in Industrial Robotic Systems

   https://doi.org/10.1049/cps2.70023  

   Shaik, Abdul_Kareem; Mohammadi, Alireza; Malik, Hafiz (May 2025, IET Cyber-Physical Systems: Theory & Applications)

   ABSTRACT Industrial robotic systems in the era of Industry 4.0 play a pivotal role in modern manufacturing. These systems, which belong to the larger class of cyber‐physical systems (CPSs), rely heavily on advanced sensing capabilities to execute complex and delicate tasks with high precision and efficiency. It is of no surprise that the integration of sensors with Industry 4.0 robotic systems exposes them to potential cyber‐physical risks/threats. This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent weaknesses into security threats. Our investigation relies on a detailed literature review of a multitude of commonly used sensors in industrial robotic systems through the lens of their physics‐based operating principles, classifications, performance limits, potential faults and associated vulnerabilities against disturbances such as temperature fluctuations, electromagnetic and acoustic interference, and ambient light variations. The result of this systematic investigation is a ring chart illustrating the overlaps and entanglements of sensor faults and performance limits, which can be exploited by cyber‐physical adversaries. Additionally, we investigate the cascading effects of compromised sensor data on the operation of industrial robotic systems through a cause‐and‐effect analysis, where the sensor vulnerabilities can cause malfunction and lead to cyber‐physical damage. The result of this analysis is a sensor cyber‐physical threat cause‐and‐effect diagram, which can be employed for design of robust and effective cyber‐physical defence measures. By providing insights into sensor‐related cyber‐risks, our cyber‐physical threat analysis paves the path for enhanced industrial robotics security. 

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