IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software Development
Jasmine Moreira
Why It Matters
What makes this one worth your time
As AI tools become more prevalent in software development, understanding and addressing their limitations is crucial for ensuring the reliability and security of AI-generated applications.
IACDM offers a structured approach to mitigate verification issues in AI-assisted software development.
Summary
The paper introduces the Interactive Adversarial Convergence Development Methodology (IACDM), a structured framework aimed at addressing the verification gap in AI-assisted software development by implementing external verification agents at discrete stages.
Key contributions
- Introduction of the IACDM framework with eight distinct phases.
- Identification of the verification gap as a structural issue in AI-assisted development.
- Proposed methodologies for deep problem discovery and systematic adversarial critique.
Notable insights
- The concept of a 'verification gap' highlights a critical oversight in current AI-assisted development practices.
- The structured framework emphasizes the importance of external verification agents, which could reshape how developers interact with AI tools.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2604.16399v2 Announce Type: replace-cross Abstract: The widespread adoption of AI-assisted development tools in 2025 -- and the emergence of vibe coding, a practice of generating complete applications from natural language without verification -- exposed a critical and tool-agnostic failure pattern: experienced developers who used frontier AI models were measurably slower in objective evaluations despite believing they were faster. Concurrently, 10.3% of AI-generated applications in a production showcase contained critical security flaws. This paper argues that these failures share a structural cause -- the verification gap: every large language model (LLM), regardless of interface or capability, operates as a stochastic generator with zero internal semantic verification capability. The tool is irrelevant; the process is determinative. We present IACDM (Interactive Adversarial Convergence Development Methodology), a structured 8-phase framework designed to address the verification gap through external verification agents (VA) operating at discrete gates. Its three pillars are: (1) deep problem discovery via Hierarchical Semantic Analysis before any technical solution; (2) persistent knowledge management across sessions; and (3) systematic adversarial critique through specialized lenses before implementation. The methodology is tool-agnostic by construction, grounded in established software engineering tradition, and applied across more than 20 projects by multiple practitioners in a production R&D environment. Limitations are formalized as testable hypotheses for future empirical validation.