The Specification Paradox: Rethinking Requirements Engineering in the Age of AI
2026-08-17 • Software Engineering
Software EngineeringProgramming Languages
AI summaryⓘ
The authors explain that while AI can help write code, it doesn't solve all the hard parts of making software. Instead, it moves the challenge to clearly understanding what the software should do and making detailed, accurate plans or specifications. They emphasize that good requirements and careful checking will become even more important as AI gets better at coding. They call this the Specification Paradox: better AI coding means we rely more on humans to create perfect instructions. So, future software development depends both on smart machines and careful human planning.
Large Language ModelsSoftware EngineeringSpecification-Driven DevelopmentRequirements EngineeringAutomation BiasSpecification OverfittingSpecification DebtSoftware EvolutionDomain UnderstandingRequirements Elicitation
Authors
Tassio Sirqueira, Jessica Faciroli
Abstract
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This article discusses the transition from a code-centered paradigm to Specification-Driven Development. We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems. Instead, it shifts this complexity toward domain understanding, requirements elicitation, specification development, validation, maintenance, and software evolution. Building on this perspective, we discuss the renewed centrality of Requirements Engineering, considering its implications for productivity and software quality, as well as risks associated with automation bias, ambiguity propagation, Specification Overfitting, and the accumulation of Specification Debt. Finally, we propose the Specification Paradox: the more capable artificial intelligence systems become at automatically generating software, the greater the dependence on correct, complete, verifiable, and explainable human-produced specifications. We conclude that the future of Software Engineering will depend not only on machines' ability to generate code, but also on humans' ability to correctly specify, evaluate, and evolve what is intended to be built.