Automated Software Testing
Overview
Automated software testing aims to check whether programs meet their requirements, behave correctly across inputs and environments, and remain reliable as they change. Approaches include model-based and specification-based testing, fuzzing, property-based and metamorphic testing, mutation analysis, search-based prioritization, and AI agents that generate tests or diagnose failures. Work increasingly applies these methods to complex settings such as web applications, APIs, autonomous systems, machine-learning software, and distributed programs, where simulations, browser traces, and runtime evidence can help expose failures. A central challenge is establishing trustworthy test oracles: coverage and passing tests do not necessarily show that a test detects real faults, while generated tests and agent judgments can be brittle, incomplete, or misleading.
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