The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memory systems. However, MPI implementations can contain defects that impact the reliability and performance of parallel applications. Detecting and correcting these defects is crucial, yet there is a lack of published models specifically designed for correcting MPI defects. To address this, we propose a model for detecting and correcting MPI defects (DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blocking point-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defects addressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and message mismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a dataset consisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes, resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI model ranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correcting defects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. The DC_MPI model fills an important research gap and provides a valuable tool for improving the quality of MPI-based parallel computing systems.

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) with BiLSTM (Bidirectional Long Short-Term Memory) and CNN (Convolutional Neural Networks) layers, to enhance classification accuracy. Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance, with precision, recall, F1-score, and accuracy of 96%, 87%, 91%, and 94%, respectively. The significant contributions of this work include a refined ABSA-based relabeling framework, the development of a high-performance classifier, and the comprehensive relabeling of the Instagram App Reviews dataset. These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.