Test case prioritization (TCP) is essential for improving testing efficiency in large-scale continuous integration (CI) environments by reducing feedback time and efficient resource usage. Machine learning (ML) has shown promise in enhancing TCP, however, demonstrating its effectiveness in production environments remains a challenge. Using the IBM Open Liberty dataset, we developed and validated an ML-based TCP framework, showing how we identified the best-performing model step by step-from feature extraction and model training to hyperparameter tuning. After validating the framework in a research setting, we deployed it in IBM's live production system. The practical implications of this study are as follows. The production results closely mirrored the research outcomes, with models trained on recent data consistently outperforming older models and non-prioritized approaches. Specifically, prioritized builds achieved a mean Average Percentage of Faults Detected (APFD) value 50% higher than that of non-prioritized builds, leading to a substantial improvement in early fault detection. The consistent improvement of models trained on newer data (M-2023) over those trained on older data (M-2022) underscores the importance of regular model updates in maintaining optimal performance. This paper comprehensively compares research and production data, illustrating how our ML-driven TCP framework ensures optimal performance and detailing the steps necessary for successful implementation in dynamic CI environments.

Continuous integration pipelines execute extensive automated test suites to validate new software builds. In this fast-paced development environment, delivering timely testing results to developers is critical to ensuring software quality. Test case prioritization (TCP) emerges as a pivotal solution, enabling the prioritization of fault-prone test cases for immediate attention. Recent advancements in machine learning have showcased promising results in TCP, offering the potential to revolutionize how we optimize testing workflows. Hyperparameter tuning plays a crucial role in enhancing the performance of ML models. However, there needs to be more work investigating the effects of hyperparameter tuning on TCP. Therefore, we explore how optimized hyperparameters influence the performance of various ML classifiers, focusing on the Average Percentage of Faults Detected (APFD) metric. Through empirical analysis of ten real-world, large-scale, diverse datasets, we conduct a grid search-based tuning with 885 hyperparameter combinations for four machine learning models. Our results provide model-specific insights and demonstrate an average 15% improvement in model performance with hyperparameter tuning compared to default settings. We further explain how hyperparameter tuning improves precision (max = 1), recall (max = 0.9633), F1-score (max = 0.9662), and influences APFD value (max = 0.9835), indicating a direct connection between tuning and prioritization performance. Hence, this study underscores the importance of hyperparameter tuning in optimizing failure prediction models and their direct impact on prioritization performance.

OBJECTIVE: The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS: A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS: After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION: The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE: This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

In the domain of autonomous vehicles, accurate modeling of the ever-changing dynamic environments is achieved using the DATMO (Detection And Tracking of Moving Objects) algorithm. This method uses input from various types of sensors and estimates their position and velocity using Kalman Filters with the integration of constant velocity model. Kalman Filters have increased in popularity as a part of robotics-related research in recent decades. The most promising applications of Kalman Filters can be seen in velocity estimation and robot localization. This paper proposes an implementation that uses point cloud data to predict the position and velocity of moving objects if and when they are detected. As demonstrated in the experimental results, the Kalman Filter accurately determines these quantities using noisy input point could data. We present a dynamic implementation, improved from a previously static implementation, for obstacle tracking. It can be helpful in automatic parking in vehicles and making decisions related to obstacle avoidance.

Atrial Fibrillation (AF) is quivering or irregular heartbeat by which the two upper chambers of the heart (atria) get affected resulting in disruption of blood flow throughout the body. If left untreated, AF can lead to several heart-related complications. In this study, we propose a signal processing model to automatically detect AF from electrocardiogram (ECG) signal at a shorter duration (9 s) that will be useful for server-based applications, remote monitoring and automated detection of AF. The ECG data collected from `CPSC-18 Challenge' have been preprocessed with FIR filter followed by the extraction of important features. RR interval (RRI) and RRI combined with mean RS ratio have particularly been found to be useful in detecting AF. Principal Component Analysis-Discriminant Analysis (PCA-DA) along with two other popular classifiers have been applied on the extracted features. The PCA-DA based approach had been superior in the detection of AF giving an accuracy of 97% with sensitivity and specificity both being 0.97. The proposed approach can be beneficial for noninvasive and faster screening of AF.