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Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
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Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource-intensive models introduces significant deployment challenges in private environments, prompting a critical question: \textit{How can personally deployable open-source LLMs achieve comparable code reasoning performance?} To this end, we propose a unified Test-Time Compute scaling framework that leverages increased inference-time computation instead of larger models. Our framework incorporates two complementary strategies: internal TTC and external TTC. Internally, we introduce a \textit{development-contextualized trajectory synthesis} method leveraging real-world software repositories to bootstrap multi-stage reasoning processes, such as fault localization and patch generation. We further enhance trajectory quality through rejection sampling, rigorously evaluating trajectories along accuracy and complexity. Externally, we propose a novel \textit{development-process-based search} strategy guided by reward models and execution verification. This approach enables targeted computational allocation at critical development decision points, overcoming limitations of existing "end-point only" verification methods. Evaluations on SWE-bench Verified demonstrate our \textbf{32B model achieves a 46\% issue resolution rate}, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1. Additionally, we provide the empirical validation of the test-time scaling phenomenon within SWE agents, revealing that \textbf{models dynamically allocate more tokens to increasingly challenging problems}, effectively enhancing reasoning capabilities. We publicly release all training data, models, and code to facilitate future research. https://github.com/yingweima2022/SWE-Reasoner
Forward citations
Cited by 7 Pith papers
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From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair
Backward reasoning distilled from a repository's already-fixed issues, injected as prompts, lifts LLM repair success on SWE-Bench Lite by up to 10.4 Pass@1 points.
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TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks
A new 530-scenario benchmark for telecom alarm root cause analysis, plus an iterative agent that lifts F1 from 58.99% to 91.79% by repeatedly repairing its code against the benchmark.
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Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs
Training on more validated GitHub issue-fix trajectories steadily raises SWE-bench Verified pass@1 from 6.4% to 38.0%, with 47.0% after test-time scaling, and no plateau yet.
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Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
A 32B model trained to self-evolve patch candidates through RL reaches 41.6% on SWE-Bench Verified with 50 samples, comparable to a 70B model using 500 samples.
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Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns
Socratic-PRMBench tests process reward models on deliberately flawed math reasoning across six reasoning patterns and reports that current PRMs score below LLM critics.
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Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
ADAPT, a diversity-aware prefix fine-tuning method, improves best-of-N sampling efficiency for a 1.5B reasoning model, reaching 80% accuracy at N=32 versus N=256 for the baseline.
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ReasonBridge: Efficient Reasoning Transfer from Closed to Open-Source Language Models
ReasonBridge distills 1,000 curated reasoning traces from a closed model into open-source models via hierarchical adapters and guided inference, claiming up to 23% benchmark gains.
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