arXiv:2607.02057v1 Announce Type: cross Abstract: In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the focus from writing precise procedures to expressing intents and goals. This paradigm shift introduces new challenges, particularly in how testing should be guided when prompts, rather than code, become primary development artifacts. To address this challenge, we propose Prompt Coverage Adequacy, a novel coverage criterion designed to support the testing of code generat
The rapid ascent of large language models and autonomous agents is forcing a re-evaluation of software development and testing paradigms, shifting focus from code to prompts.
This development addresses a critical challenge in the burgeoning AI agentic paradigm: ensuring reliability and safety when intent, rather than explicit procedure, dictates software behavior.
Software development shifts further towards intent-based prompting, requiring new methodologies for testing and quality assurance that are fundamentally different from traditional code-based testing.
- · AI agent developers
- · Software testing industry
- · AI safety researchers
- · LLM providers
- · Traditional software testing frameworks
- · Developers solely focused on imperative coding
- · Organizations slow to adapt to prompt-driven development
New tools and standards will emerge for prompt engineering and testing, becoming integral to AI system development lifecycles.
The cost and complexity of developing and assuring autonomous AI agents could decrease, accelerating their adoption across various industries.
Legal and regulatory frameworks for AI systems will likely incorporate prompt coverage and assurance metrics as part of compliance standards.
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Read at arXiv cs.AI