Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.303189

Artificial Intelligence in Software Testing and Beyond: A Review of Current Practices and Emerging Challenges

Codrina-Victoria Lisaru ORCID..., Claudiu-Vasile Kifor ORCID...
Department of Industrial Engineering and Management, Faculty of Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania

Background: Artificial intelligence (AI) is increasingly used both to test software (T1) and to assure AI-based systems (T2), with adjacent software-engineering work that shapes testing practice (T3). Prior reviews are mostly descriptive and rarely report comparable maturity or replicability signals.

Objective: To provide a PRISMA-style systematic review (2015–2025, Web of Science) that maps T1–T2–T3 within a testing-centric frame, audits evidence maturity, threats reporting, and artefact openness per paper, and adds an explicit lens of large language models or generative AI (LLMs/GenAI).

Methods: We queried the Web of Science Core Collection (2015–2025), screened via a predefined protocol, and extracted ten items (D1–D10) per study to normalize comparisons. Seventy-two papers met the criteria. Findings are organized into three themes: (T1) AI-based software testing, (T2) testing/validation of AI systems, and (T3) AI-related software engineering topics with implications for testing—T3 corresponding to the “beyond” in the paper’s title.

Results: The corpus is limited in practice-oriented evidence: 31 laboratory/simulation, 3 industrial, 10 hybrid, 6 conceptual/guideline and 22 secondary studies. Only 18/72 provide public artefacts; 33/72 report no empirical metrics. By theme, T1=32, T2=15, T3=25; the LLMs/GenAI subset totals 10 papers. Openness strongly co-occurs with measurable outcomes (88.9% of artefact-sharing papers report metrics vs 42.6% without), yet “all-three credible” studies (industrial/hybrid + open artefacts + metrics) are rare (4/72 overall; 1/10 for LLMs/GenAI).

Conclusion: AI shows promise for testing, but evidence remains thin on industrial adoption and reproducibility. We recommend prioritizing hybrid/industrial validations, releasing artefacts by default, and using standardized task–metric bundles. The review presents T1 and T2 results, separates T3 for scope clarity, and provides actionable maturity and replicability signals to guide responsible, empirical adoption.

Keywords: Software testing; Artificial intelligence; AI; AI-driven testing; Software engineering; Requirements engineering; Human-AI collaboration; Software quality; Large Language Models; LLMs.

Received: August 29, 2025; Revised: January 8, 2026; Accepted: January 19, 2026; Prepublished online: April 9, 2026 

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