Comprehensive Review on Innovation in Software Testing: Advancing Methods with State-of-the-Art Intelligent Platforms
Keywords:
AI-driven testing, autonomous quality assurance, cloud-based testing, DevOps, intelligent automation, test orchestrationAbstract
Software engineering and DevOps keep changing faster than most teams can catch their breath, and that shift has pushed testing into new territory. What used to be a fairly manual, rule-based practice is now leaning hard on AI to make sense of growing complexity. This paper takes a close look at how intelligent testing frameworks those that use machine learning, natural language processing, and other AI techniques fit into modern CI/CD pipelines. It walks through how testing evolved from scripted checks to systems that can, at least in theory, adapt and even repair themselves. The review digs into the architectures and algorithms behind these systems and how they’re actually used in industry. Some approaches, like reinforcement learning or NLP-based test generation, appear to raise fault detection rates and keep pipelines running with fewer interruptions. That said, the story isn’t all progress. These systems depend heavily on data quality, and bias or noise can easily skew predictions. Scalability sounds great in principle, but real-world environments often complicate it. The paper argues that explainability and ongoing validation aren’t just nice-to-haves they’re what make autonomous testing trustworthy. From a broader view, intelligent testing seems to shift quality assurance from catching errors after the fact to something more proactive and self-correcting. Whether that transformation truly holds up across teams and toolchains, though, still depends on how well the AI can align with human judgment. The work here nudges the field closer to testing ecosystems that are resilient, transparent, and hopefully just a bit more human-aware.
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A. Kiran, W. H. Butt, M. W. Anwar, F. Azam, and B. Maqbool, “A comprehensive investigation of modern test suite optimization trends, tools and techniques,” IEEE Access, vol. 7, pp. 89093–89117, 2019.
M. Boukhlif et al., “Exploring the application of classical and intelligent software testing in medicine: a literature review,” in Proc. Int. Conf. Advanced Intelligent Systems for Sustainable Development, 2024, pp. 37–46.
J. Wang et al., “Software testing with large language models: Survey, landscape, and vision,” IEEE Trans. Softw. Eng., vol. 50, no. 4, pp. 911–936, 2024.
A. Mehmood, Q. M. Ilyas, M. Ahmad, and Z. Shi, “Test suite optimization using machine learning techniques: A comprehensive study,” IEEE Access, vol. 12, pp. 168645–168671, 2024.
K. Telli et al., “A comprehensive review of recent research trends on unmanned aerial vehicles (UAVs),” Systems, vol. 11, no. 8, p. 400, 2023.
T. Bakhshi, “State of the art and recent research advances in software defined networking,” Wireless Commun. Mobile Comput., vol. 2017, no. 1, p. 7191647, 2017.
Y. Kumar et al., “A comprehensive review of AI advancement using testFAILS and testFAILS-2 for the pursuit of AGI,” Electronics, vol. 13, no. 24, p. 4991, 2024.
Q. Abbas, M. E. A. Ibrahim, and M. A. Jaffar, “A comprehensive review of recent advances on deep vision systems,” Artif. Intell. Rev., vol. 52, no. 1, pp. 39–76, 2019.
A. Salahirad, G. Gay, and E. Mohammadi, “Mapping the structure and evolution of software testing research over the past three decades,” J. Syst. Softw., vol. 195, p. 111518, 2023.
P. Chamoso, A. González-Briones, S. Rodríguez, and J. M. Corchado, “Tendencies of technologies and platforms in smart cities: a state-of-the-art review,” Wireless Commun. Mobile Comput., vol. 2018, no. 1, p. 3086854, 2018.
D. Bonino, A. Ciaramella, and F. Corno, “Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics,” World Patent Inf., vol. 32, no. 1, pp. 30–38, 2010.
K. Y. H. Lim, P. Zheng, and C.-H. Chen, “A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives,” J. Intell. Manuf., vol. 31, no. 6, pp. 1313–1337, 2020.
W. S. Robert et al., “A comprehensive review on cryptographic techniques for securing internet of medical things: A state-of-the-art, applications, security attacks, mitigation measures, and future research direction,” Mesopotamian J. Artif. Intell. Healthc., pp. 135–169, 2024.
V. Kumar et al., “The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management,” Sustainability, vol. 15, no. 13, p. 10543, 2023.
V. Vyatkin, “Software engineering in industrial automation: State-of-the-art review,” IEEE Trans. Ind. Inf., vol. 9, no. 3, pp. 1234–1249, 2013.
A. Singh et al., “Sidechain technologies in blockchain networks: An examination and state-of-the-art review,” J. Netw. Comput. Appl., vol. 149, p. 102471, 2020.
S. Sonko et al., “A comprehensive review of embedded systems in autonomous vehicles: Trends, challenges, and future directions,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 2009–2020, 2024.
M. Ryalat et al., “Research and education in robotics: A comprehensive review, trends, challenges, and future directions,” J. Sensor Actuator Netw., vol. 14, no. 4, p. 76, 2025.
B. Mendu and N. Mbuli, “State-of-the-art review on the application of unmanned aerial vehicles (UAVs) in power line inspections,” Drones, vol. 9, no. 4, p. 265, 2025.
B. P. Bhattarai et al., “Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions,” IET Smart Grid, vol. 2, no. 2, pp. 141–154, 2019.
P. Ghamisi et al., “Multisource and multitemporal data fusion in remote sensing: A comprehensive review,” IEEE Geosci. Remote Sens. Mag., vol. 7, no. 1, pp. 6–39, 2019.
W. S. Admass, Y. Y. Munaye, and A. A. Diro, “Cyber security: State of the art, challenges and future directions,” Cyber Secur. Appl., vol. 2, p. 100031, 2024.
G. Abdelkader, K. Elgazzar, and A. Khamis, “Connected vehicles: Technology review, state of the art, challenges and opportunities,” Sensors, vol. 21, no. 22, p. 7712, 2021.
Y. J. Qu, X. G. Ming, Z. W. Liu, X. Y. Zhang, and Z. T. Hou, “Smart manufacturing systems: state of the art and future trends,” Int. J. Adv. Manuf. Technol., vol. 103, no. 9, pp. 3751–3768, 2019.
S. S. Ali and B. J. Choi, “State-of-the-art artificial intelligence techniques for distributed smart grids: A review,” Electronics, vol. 9, no. 6, p. 1030, 2020.
H. Sohrabi et al., “State of the art: Lateral flow assays toward point‐of‐care foodborne pathogenic bacteria detection in food samples,” Compr. Rev. Food Sci. Food Saf., vol. 21, no. 2, pp. 1868–1912, 2022.
J. Leaman and H. M. La, “A comprehensive review of smart wheelchairs: past, present, and future,” IEEE Trans. Hum.-Mach. Syst., vol. 47, no. 4, pp. 486–499, 2017.
L. Briand and Y. Labiche, “Empirical studies of software testing techniques: Challenges, practical strategies, and future research,” ACM SIGSOFT Softw. Eng. Notes, vol. 29, no. 5, pp. 1–3, 2004.
V. Garousi and J. Zhi, “A survey of software testing practices in Canada,” J. Syst. Softw., vol. 86, no. 5, pp. 1354–1376, 2013.
O. A. L. Lemos et al., “Evaluation studies of software testing research in Brazil and in the world: A survey,” J. Syst. Softw., vol. 86, no. 4, pp. 951–969, 2013.
V. Garousi et al., “Exploring the industry's challenges in software testing: An empirical study,” J. Softw. Evol. Process, vol. 32, no. 8, p. e2251, 2020.
C. Kaner, “Fundamental challenges in software testing,” presented at Butler Univ. Colloquium, 2003.
P. K. Waychal and L. F. Capretz, “Why a testing career is not the first choice of engineers,” arXiv preprint arXiv:1612.00734, 2016.
P. Raulamo-Jurvanen, S. Hosio, and M. V. Mäntylä, “Practitioner evaluations on software testing tools,” in Proc. 23rd Int. Conf. Eval. Assessment Softw. Eng., 2019, pp. 57–66.
K. Ibrahim and J. A. Whittaker, “Model-based software testing,” in Encyclopedia of Software Engineering. New York: Wiley, 2001.
G. D. Everett and R. McLeod Jr., Software Testing: Testing Across the Entire Software Development Life Cycle. Wiley, 2007.
N. Juristo, A. M. Moreno, and S. Vegas, “Towards building a solid empirical body of knowledge in testing techniques,” ACM SIGSOFT Softw. Eng. Notes, vol. 29, no. 5, pp. 1–4, 2004.
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