# What to Cut? Predicting Unnecessary Methods in Agentic Code Generation

arXiv: https://arxiv.org/abs/2602.17091 · HTML: https://mlsanity.com/paper/2602.17091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [A Taxonomy of Inefficiencies in LLM-Generated Python Code](https://mlsanity.com/paper/2503.06327/) (2025)
- [Investigating The Smells of LLM Generated Code](https://mlsanity.com/paper/2510.03029/) (2025)
- [DCE-LLM: Dead Code Elimination with Large Language Models](https://mlsanity.com/paper/2506.11076/) (2025)
- [Are Classical Clone Detectors Good Enough For the AI Era?](https://mlsanity.com/paper/2509.25754/) (2025)
- [ActRef: Enhancing the Understanding of Python Code Refactoring with Action-Based Analysis](https://mlsanity.com/paper/2505.06553/) (2025)

## Built on by (most cited first, 6 of 6)

- [SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks](https://mlsanity.com/paper/2603.24755/) (2026)
- [To Add Is Machine, To Delete Is Human: Measuring and Mitigating Deletion Avoidance in LLM Code Editing](https://mlsanity.com/paper/2607.28887/) (2026)
- [Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration](https://mlsanity.com/paper/2606.19042/) (2026)
- [TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization](https://mlsanity.com/paper/2607.18161/) (2026)
- [Can Agents Design Libraries for Agents?](https://mlsanity.com/paper/2609.36730/) (2026)
- [SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents](https://mlsanity.com/paper/2610.18206/) (2026)

## Abstract (arXiv)

Agentic Coding, powered by autonomous agents such as GitHub Copilot and Cursor, enables developers to generate code, tests, and pull requests from natural language instructions alone. While this accelerates implementation, it produces larger volumes of code per pull request, shifting the burden from implementers to reviewers. In practice, a notable portion of AI-generated code is eventually deleted during review, yet reviewers must still examine such code before deciding to remove it. No prior work has explored methods to help reviewers efficiently identify code that will be removed.In this paper, we propose a prediction model that identifies functions likely to be deleted during PR review. Our results show that functions deleted for different reasons exhibit distinct characteristics, and our model achieves an AUC of 87.1%. These findings suggest that predictive approaches can help reviewers prioritize their efforts on essential code.