# Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration

arXiv: https://arxiv.org/abs/2606.19042 · HTML: https://mlsanity.com/paper/2606.19042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review](https://mlsanity.com/paper/2510.00328/) (2025)
- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda](https://mlsanity.com/paper/2507.21928/) (2025)
- [Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding](https://mlsanity.com/paper/2509.12491/) (2025)
- [More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests](https://mlsanity.com/paper/2601.21276/) (2026)
- [What to Cut? Predicting Unnecessary Methods in Agentic Code Generation](https://mlsanity.com/paper/2602.17091/) (2026)
- [Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding](https://mlsanity.com/paper/2512.22418/) (2025)
- [Specialization of Run-time Configuration Space at Compile-time: An Exploratory Study](https://mlsanity.com/paper/2210.14082/) (2022)
- [Small Yet Configurable: Unveiling Null Variability in Software](https://mlsanity.com/paper/2604.15957/) (2026)

## Abstract (arXiv)

In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a natural language prompt, but what happens to the variability that traditional software engineering carefully builds into code? To answer this question, we conducted an exploratory analysis on 10 vibe coded C/C++ projects, which suggests that there is near zero in-artifact variability, i.e., at compile- and runtime. All variability decisions are resolved at a single new binding time, generation time, the moment the LLM produces the source code. Rather than treating this as a defect to fix, we propose Variability by Regeneration (VbR), to our knowledge the first product line approach in which the LLM acts as the derivation engine, generating a dedicated, free of dead code binary for each variant from a declarative specification, while a variant dispatcher transparently routes user requests to the matching binary. We formalise VbR, contrast it with classical SPL derivation, and demonstrate its full pipeline on a wc product family. For SPL engineering, variability in AI-generated software belongs in the specification, not in the code.