Special Issue "Recent Trends in Software Engineering and Applications"

Submission Deadline: 30 October 2021 (closed)
Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Basit Shahzad, National University of Modern Languages, Islamabad.
Dr. Asadullah shaikh, Najran University, Saudi Arabia.

Summary

Software Engineering is an engineering discipline for the development and operation of the software. This discipline is gradually changing the way humans interact with technology and the way society functions. It involves programming languages, databases, software development tools, system platforms, standards, design patterns, and so on.

Nowadays, computer software finds applications in almost all industries, such as manufacturing, agriculture, banking, aviation, and government departments. Furthermore, the software plays an extremely important role in scientific research: instruments control software, data analysis, scientific results publishing. These applications have promoted the development of the economy and society as well as the efficiency of work and life.

 

This Special Issue aims to advance the state of the art by gathering original research in the field of software-intensive systems, fundamental connections between software engineering and information theory, and, especially, sustainable software product lines.


Keywords
Mathematics and Computer Science
Software Industry
Software Engineering
Software for the Science Research

Published Papers
  • The Double Edge Sword Based Distributed Executor Service
  • Abstract Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate… More
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  • An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining
  • Abstract Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model. It presents a… More
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  • Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service
  • Abstract The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical… More
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  • Defect Prediction Using Akaike and Bayesian Information Criterion
  • Abstract Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To… More
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