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Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios. [ABSTRACT FROM AUTHOR]

Threatening public health, pulmonary disease (PD) encompasses diverse lung injuries like chronic obstructive PD, pulmonary fibrosis, asthma, pulmonary infections due to pathogen invasion, and fatal lung cancer. The crucial involvement of RNA epigenetic modifications in PD pathogenesis is underscored by robust evidence. These modifications not only shape cell fates but also finely modulate the expression of genes linked to disease progression, suggesting their utility as biomarkers and targets for therapeutic strategies. The critical RNA modifications implicated in PDs are summarized in this review, including N6‐methylation of adenosine, N1‐methylation of adenosine, 5‐methylcytosine, pseudouridine (5‐ribosyl uracil), 7‐methylguanosine, and adenosine to inosine editing, along with relevant regulatory mechanisms. By shedding light on the pathology of PDs, these summaries could spur the identification of new biomarkers and therapeutic strategies, ultimately paving the way for early PD diagnosis and treatment innovation. [ABSTRACT FROM AUTHOR]