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    A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and Opportunities
    (Institute of Electrical and Electronics Engineers Inc, 2024) Kalan, Reza; Dülger, İsmail
    With the emergence of new video streaming technologies, advancements in networking paradigms, and the increasing popularity of mobile and smart devices, we are witnessing phenomenal growth in live video traffic over the Internet. To effectively address the explosive growth of multimedia applications over the Internet, it is crucial to consider scalability, quality, and security. The reliability of HTTP Adaptive Streaming (HAS), which leverages TCP, encourages many Over-the-Top (OTT) providers to adopt progressive streaming technology. Monitoring network traffic patterns and client behaviors provides client-side players with greater intelligence to adapt suitable video quality. However, the inflexibility and inefficiency of legacy networks and streaming applications often diminish the perceived streaming quality for clients. This study aims to explore the interplay between machine learning, emerging network architectures, and streaming technology paradigms. Furthermore, it survey the technical challenges within the adaptive video streaming and content delivery technologies, where adaptive streaming leverages advancements in network and artificial intelligence paradigms, edge computing, and NFV-SDN technologies to better adapt to network dynamics and enhance Quality of Experience (QoE). This study focuses exclusively on papers published in the last five years. © 2024 IEEE.
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    Constraint-based heuristic algorithms for software test generation
    (Elsevier, 2024) Arasteh, Bahman; Aghaei, Babak; Ghanbarzadeh, Reza; Kalan, Reza
    While software testing is essential for enhancing a software system's quality, it can be time-consuming and costly during developing software. Automation of software testing can help solve this problem, streamlining time-consuming testing tasks. However, generating automated test data that maximally covers program branches is a complex optimization problem referred to as NP-complete and should be addressed appropriately. Although a variety of heuristic algorithms have already been suggested to create test suites with the greatest coverage, they have issues such as insufficient branch coverage, low rate of success in generating test data with high coverage, and unstable results. The main objective of the current chapter is to investigate and compare the coverage, success rate (SR), and stability of various heuristic algorithms in software structural test generation. To achieve this, the effectiveness of seven algorithms, genetic algorithm (GA), simulated annealing (SA), ant colony optimizer (ACO), particle swarm optimizer (PSO), artificial bee colony (ABC), shuffle frog leaping algorithm (SFLA), and imperialist competitive algorithm (ICA), are examined in automatically generating test data, and their performance is compared on the basis of various criteria. The experiment results demonstrate the superiority of the SFLA, ABC, and ICA to other examined algorithms. Overall, SFLA outperforms all other algorithms in coverage, SR, and stability. © 2024 Elsevier Inc. All rights reserved.