# Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2601.11655 · HTML: https://mlsanity.com/paper/2601.11655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2026.

## Built on by (most cited first, 10 of 10)

- [Code as Agent Harness](https://mlsanity.com/paper/2605.18747/) (2026)
- [SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training](https://mlsanity.com/paper/2602.03411/) (2026)
- [Goedel-Code-Prover: Hierarchical Proof Search for Open State-of-the-Art Code Verification](https://mlsanity.com/paper/2603.19329/) (2026)
- [SWE-Prime: Fewer Trajectories, Better Performance](https://mlsanity.com/paper/2608.27449/) (2026)
- [Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution](https://mlsanity.com/paper/2604.05955/) (2026)
- [Agentic Coding Needs Proactivity, Not Just Autonomy](https://mlsanity.com/paper/2605.06717/) (2026)
- [Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development](https://mlsanity.com/paper/2605.20456/) (2026)
- [Long-Horizon Analog Design Bench: Benchmarking Agents on Hours-Long Analog and Mixed-Signal Circuit Design Tasks](https://mlsanity.com/paper/2609.33356/) (2026)
- [RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair](https://mlsanity.com/paper/2610.04658/) (2026)
- [A Decade in a Day: Synthesizing Agent-Native Interfaces from Human-Oriented Software Evolution](https://mlsanity.com/paper/2610.15765/) (2026)

## Abstract (arXiv)

Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment of benchmarks like SWE-bench revealed this task as profoundly difficult for large language models, thereby significantly accelerating the evolution of autonomous coding agents. This paper presents a systematic survey of this emerging domain. We begin by examining data construction pipelines, covering automated collection and synthesis approaches. We then provide a comprehensive analysis of methodologies, spanning training-free frameworks with their modular components to training-based techniques, including supervised fine-tuning and reinforcement learning. Subsequently, we discuss critical analyses of data quality and agent behavior, alongside practical applications. Finally, we identify key challenges and outline promising directions for future research. An open-source repository is maintained at https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution to serve as a dynamic resource in this field.