Context: The increasing demand for high-performance computing has established General-Purpose Graphics Processing Units () as a cornerstone of modern parallelism, successfully circumventing the scalability challenges presented by Amdahl’s Law. However, efficiently translating hardware potential into realized performance requires specialized programming knowledge. This paper addresses the gap between architectural capabilities and accessible, strategic programming methodologies.

Methods: We conducted a systematic review and conceptual synthesis of GPGPU programming strategies, categorizing them into -centric (data locality, coalescing) and -centric (parallel decomposition, divergence minimization) paradigms. The analysis links these strategies directly to fundamental architectural primitives, such as the memory hierarchy and Streaming Multiprocessors. A comparative analysis is introduced, contrasting the GPGPU's throughput-centric design with the latency-centric architecture of many-core x86 systems. Performance implications are discussed through the lens of established optimization principles.

Results: Strategic programming decisions—particularly those concerning the effective utilization of and the minimization of —are demonstrated to be the dominant factors in performance scaling. Techniques like parallel reduction and algorithmic auto-tuning consistently yield order-of-magnitude improvements over naive implementations. The efficacy of these strategies is shown to be critically dependent on evolving architectural features, necessitating a fundamental understanding of the core architectural divergence from traditional computing.

Conclusion: Maximizing the potential of GPGPU computing hinges on the developer’s ability to implement . While high-level tools are emerging, the immediate future of extreme performance lies in a rigorous, strategic approach to code design. Future research must focus on simplifying this complexity through $\mathbf{ML \text{-driven } auto\text{-tuning}$ and standardized higher-level abstraction models.

Purpose: This paper presents a systematic review and comparative analysis of the Amazon Web Services (AWS) cloud platform, focusing on its foundational architecture, economic implications, and persistent challenges, to guide strategic adoption and define future research trajectories.

Design/Methodology/Approach: We employ a systematic review methodology, synthesizing evidence from a curated selection of foundational academic and authoritative industry literature [4, 5, 6]. The analysis is structured across three core dimensions: architectural components, economic models, and future strategic considerations. A comparative approach is used to contrast the cloud model with traditional on-premise infrastructure[18,19].

Findings: The findings highlight that AWS’s dominance stems from its highly redundant global infrastructure, which offers unparalleled scalability and resilience, effectively shifting capital expenditure (CapEx) to operational expenditure (OpEx). However, this ecosystem introduces complexity in cost management (FinOps) and necessitates a strict adherence to the Shared Responsibility Model to maintain a secure posture [5]. Analysis of market data confirms its sustained leadership position [8]. Mastering the complex usage-based billing requires the implementation of advanced FinOps frameworks, leveraging automation and AI to ensure continuous cost optimization and governance.

Originality/Value: This article addresses key literature gaps by providing a holistic, three-dimensional framework for understanding the AWS ecosystem, offering both a technical overview and a strategic decision-making guide for researchers and practitioners. It explicitly outlines future research needs, particularly in empirical performance validation and sustainable cloud computing practices.

Background: The increasing complexity of software systems necessitates robust and efficient testing methods. While Search-Based Software Testing (SBST) has emerged as a powerful technique for automated test case generation, its effectiveness can be limited by its singular focus on code coverage. The generated tests, although structurally sound, may not target the most fault-prone areas of the code.

Aim: This study aims to address this limitation by proposing and empirically investigating a novel approach that integrates defect prediction (DP) models to guide the search process of SBST. By leveraging insights from historical code data, our method prioritizes the generation of test cases for code modules identified as having a higher likelihood of containing defects.

Method: We conducted a large-scale empirical study using 20 real-world, open-source Java projects from the Defects4J database. We developed a machine learning-based defect prediction model to identify fault-prone files. We then implemented a new fitness function for the EvoSuite test generation tool that incorporates the prediction score. The performance of this defect prediction-guided SBST approach was compared against a traditional, coverage-based SBST approach, using metrics of fault detection effectiveness and computational efficiency.

Results: Our findings indicate that the proposed DP-guided SBST approach significantly outperforms the traditional method in terms of the number of unique faults detected. Statistical analysis revealed a strong positive effect size for our approach. While there was a slight increase in computational overhead associated with the defect prediction component, it was minimal relative to the substantial gain in fault detection.

Conclusion: The results demonstrate that integrating defect prediction into the search-based test generation process is a highly effective strategy for improving the overall quality and fault-finding capability of automated testing. This approach represents a promising direction for enhancing software testing practices, particularly in continuous integration environments.