AI-Driven Software Testing for a Test Center of Excellence: A Systematic Mapping and Evidence Synthesis

Abstract

Artificial intelligence (AI) is increasingly used to automate and optimize software testing, but the evidence is distributed across heterogeneous testing activities, AI techniques, and evaluation measures. This study maps AI-driven software-testing research and translates the evidence into capabilities relevant to a Test Center of Excellence (TCoE). A systematic evidence workflow was applied to a unified audit corpus of 2,072 records, followed by focused eligibility reconciliation and full-text verification. The final focused evidence workset contains 29 records: 15 primary empirical studies, 9 secondary studies, 1 scope exclusion, and 4 full-text/access exclusions. The primary evidence covers machine learning, reinforcement learning, evolutionary and search-based methods, deep learning, explainable AI, and large language models. The supported testing activities include test generation, selection and prioritization, minimization and optimization, fuzzing, validation, adaptive automation, and maintenance. The synthesis shows that AI should be treated in a TCoE as a governed capability layer rather than as a single automation tool. The proposed capability model links AI techniques to testing activities, measurable outcomes, reusable TCoE capabilities, and governance with human oversight. The study also identifies gaps in reliability, context awareness, test-oracle support, long-term maintenance, CI/CD integration, reproducibility, and standardized evaluation.