[Paper] The Evolutionary Ecology of Software: Constraints, Innovation, and the AI Disruption

Source: arXiv - 2512.02953v1

Overview

The paper “The Evolutionary Ecology of Software: Constraints, Innovation, and the AI Disruption” treats software systems as living ecosystems that evolve under pressures of constraint, tinkering, and competition. By blending evolutionary theory, network science, and agent‑based simulations, the authors show how programming languages, libraries, and AI‑assisted tools co‑evolve with developer behavior and broader cultural norms—offering a fresh lens for anyone building or managing modern software.

Key Contributions

• Ecological framing of software evolution – Introduces concepts such as frequency‑dependent selection and niche construction to explain why some languages thrive while others fade.
• Hybrid modeling approach – Combines agent‑based simulations with empirical case studies (e.g., the rise of Python, the decline of Perl) to capture both micro‑level developer decisions and macro‑level ecosystem dynamics.
• Network‑based analysis of software ecosystems – Uses complex‑network metrics (centrality, modularity, robustness) to quantify how tightly coupled libraries and tools shape evolutionary pathways.
• AI‑driven development as a disruptive evolutionary force – Provides a theoretical and empirical assessment of how large language models (LLMs) alter the balance between novelty generation (creation) and imitation (reuse).
• Warning about “cultural stagnation” – Draws parallels between reduced biodiversity in biological ecosystems and the potential homogenization of codebases when AI suggestions dominate.

Methodology

1. Agent‑Based Model (ABM) – Simulated a population of “developers” who choose, modify, or abandon software artifacts (languages, libraries) based on payoff functions that encode utility, social prestige, and compatibility constraints.
2. Frequency‑Dependent Selection – The payoff for adopting a technology increases with its current popularity (network effects) but also incurs a novelty penalty to capture the cost of learning new tools.
3. Empirical Case Studies – Tracked historical adoption curves of several programming languages and major frameworks using GitHub, Stack Overflow, and package‑manager data.
4. Network Analysis – Constructed dependency graphs (e.g., npm, PyPI) and measured structural properties over time to identify “keystone” packages that act as ecosystem engineers.
5. AI‑Disruption Scenario – Added a “LLM assistant” agent that can auto‑generate code snippets, reducing the learning cost for popular patterns but also biasing developers toward the most frequently suggested solutions.

The combination of simulation and real‑world data lets the authors validate theoretical predictions while keeping the model grounded in observable developer behavior.

Results & Findings

Practical Implications

• Tooling Strategy – Companies should monitor the centrality of third‑party dependencies; diversifying across multiple “keystone” libraries can mitigate supply‑chain risk.
• AI‑Assisted Development Policies – Encourage developers to review and customize LLM‑generated code rather than blindly accepting suggestions, preserving diversity of solutions.
• Language & Framework Adoption Roadmaps – Early‑stage investment in emerging languages (e.g., Go, Kotlin) can be justified if they occupy a niche with low competition, potentially yielding long‑term strategic advantage.
• Ecosystem Health Dashboards – Implement metrics (e.g., language diversity index, dependency network robustness) in CI/CD pipelines to detect early signs of lock‑in or stagnation.
• Open‑Source Governance – Funding and maintaining “keystone” projects (like react, express) becomes a public‑good activity; loss of such projects could destabilize large swaths of the software ecosystem.

Limitations & Future Work

• Model Simplifications – The ABM abstracts away many real‑world factors (e.g., corporate licensing, regulatory constraints) that can also drive adoption.
• Data Bias – Reliance on public repositories may under‑represent proprietary or niche ecosystems, skewing the measured diversity.
• LLM Behavior Evolution – The study treats LLMs as static assistants; future work should model how LLMs themselves evolve (fine‑tuning, reinforcement learning) and feed back into the ecosystem.
• Human Factors – Psychological aspects of developer trust and creativity are only indirectly captured; integrating surveys or ethnographic studies could enrich the model.

Bottom line: By viewing software as an evolving ecological system, the paper equips developers, tech leads, and product managers with a macro‑level perspective on how choices today shape the health and adaptability of tomorrow’s codebases—especially in an era where AI tools are becoming integral collaborators.

Authors

• Sergi Valverde
• Blai Vidiella
• Salva Duran‑Nebreda

Paper Information

• arXiv ID: 2512.02953v1
• Categories: cs.SE, cond-mat.dis-nn
• Published: December 2, 2025
• PDF: Download PDF