Papers with software engineering tasks
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)
Copied to clipboard
Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley
| Challenge: | Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning. |
| Approach: | They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
| Outcome: | The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
Disentangled Code Representation Learning for Multiple Programming Languages (2021.findings-acl)
Copied to clipboard
| Challenge: | Developing effective distributed representations of source code is challenging . current code embedding approaches that represent the semantic and syntax of code are less interpretable . |
| Approach: | They propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting. |
| Outcome: | The proposed approach achieves better interpretability and generalizability over existing methods. |
RExBench: Can coding agents autonomously implement AI research extensions? (2026.acl-long)
Copied to clipboard
| Challenge: | Existing large language model (LLM) agents are not capable of performing research extension tasks autonomously. |
| Approach: | They propose a benchmark to evaluate LLM agents' ability to extend existing AI research . they use extensions of 12 recently published research papers accompanied by domain expert-written instructions . |
| Outcome: | The proposed benchmark evaluates 12 LLM agents implemented using aider and OpenHands. |
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)
Copied to clipboard
Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen
| Challenge: | Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge. |
| Approach: | They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance. |
| Outcome: | Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points. |
Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have limited fault localization capabilities due to limited context length. |
| Approach: | They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs. |
| Outcome: | The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset. |
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)
Copied to clipboard
Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks. |
| Approach: | They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation. |
| Outcome: | EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% . |
OmniCode: A Benchmark for Evaluating Software Development Agents (2026.findings-acl)
Copied to clipboard
Atharv Sonwane, Eng-Shen Tu, Wei-Chung Lu, Claas Beger, Carter Larsen, Debjit Dhar, Simon Alford, Rachel Chen, Ronit Pattanayak, Tuan Anh Dang, Guohao Chen, Gloria Geng, Kevin Ellis, Saikat Dutta
| Challenge: | popular coding benchmarks focus on narrowly scoped tasks such as competition programming and patch generation. |
| Approach: | They propose a software engineering benchmark that aims to provide a broader set of tasks beyond code or patch generation. |
| Outcome: | The proposed framework performs well on bug fixing for Python, test generation, code review fixing, and style fixing with popular agent frameworks such as SWE-Agent. |