This article presents an integrative, comprehensive, and forward-looking examination of software testing strategies in the context of rapid methodological and technological shifts, particularly the emergence of large language models (LLMs) and serverless architectures. Grounded strictly in the supplied literature, the paper synthesizes empirical findings, theoretical frameworks, and practical considerations to articulate a cohesive research narrative that spans four decades of testing evolution, contemporary automation frameworks, and emerging evaluation challenges introduced by LLM-assisted development and testing. The abstract summarizes the study's motivation, approach, key findings, and implications. Motivation: software testing remains a decisive factor in software quality assurance amid rapidly changing development paradigms and new testing affordances driven by AI (Gurcan et al., 2022; Wang et al., 2024). Approach: the paper undertakes a methodical scholarly integration of domain surveys, empirical studies on developer behavior and job profiles, and recent investigations into LLMs and serverless testing to build a conceptual and practical framework for adaptive testing. Key findings: (1) testing strategies have evolved from predominantly manual, artefact-centric approaches to hybrid, automation-enabled frameworks with strong semantic and traceability emphases (Ricca & Tonella, 2001; Andrews et al., 2005; Gurcan et al., 2022); (2) organizational and industrial constraints shape which testing practices are adoptable and sustainable, and job profile analyses reveal a strong gap between academic proposals and industrial adoption (Kassab et al., 2021; Alshahwan et al., 2023); (3) serverless architectures and LLMs introduce new testing vectors and complexity, including ephemeral execution contexts and probabilistic output behaviors that require novel testing heuristics and evaluation metrics (De Silva & Hewawasam, 2024; Wang et al., 2024); and (4) empirical studies on how developers craft tests provide actionable micro-level insights into the cognitive and social processes underpinning test design (Aniche et al., 2021; Pudlitz et al., 2020). Implications: the article proposes a layered, traceability-first testing framework that integrates lightweight requirements annotation, LLM-assisted test generation, and continuous empirical feedback loops tailored to organizational capacity. The framework is evaluated against documented industrial challenges and research gaps, yielding a prioritized research agenda and a set of operational recommendations for practitioners and researchers. Concluding remarks: to achieve resilient, efficient, and trustworthy testing in contemporary environments, coordinated advances in tooling, human-centered process redesign, and empirical evaluation are necessary (Putra et al., 2023; Zhao et al., 2024).

Software testing strategies, large language models, serverless testing, test automation

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