Dynamic analysis enhances issue resolution
- URL: http://arxiv.org/abs/2603.22048v1
- Date: Mon, 23 Mar 2026 14:48:54 GMT
- Title: Dynamic analysis enhances issue resolution
- Abstract summary: DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
- Score: 53.50448142467294
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Translating natural language descriptions into viable code fixes remains a fundamental challenge in software engineering. While the proliferation of agentic large language models (LLMs) has vastly improved automated repository-level debugging, current frameworks hit a ceiling when dealing with sophisticated bugs like implicit type degradations and complex polymorphic control flows. Because these methods rely heavily on static analysis and superficial execution feedback, they lack visibility into intermediate runtime states. Consequently, agents are forced into costly, speculative trial-and-error loops, wasting computational tokens without successfully isolating the root cause. To bridge this gap, we propose DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent), a pioneering automated repair framework that natively embeds dynamic analysis into the agent's reasoning cycle. Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data -- such as variable mutations and call stacks -- and synthesizes them into structured semantic reports. This mechanism fundamentally shifts the agent's behavior from blind guesswork to evidence-based, deterministic deduction. When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset. Compared to existing baselines, our framework not only conquers highly complex defects but also cuts overall inference expenses by roughly 10% and decreases input token consumption by approximately 25%.
Related papers
- ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
arXiv Detail & Related papers (2026-08-03T15:07:46Z) - Constraint Decay: The Fragility of LLM Agents in Backend Code Generation [9.659020624935687]
Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications.<n>Production-grade software requires strict adherence to structural constraints, such as architectural patterns, databases, and object-relational mappings.<n>We present a systematic study evaluating how well agents handle structural constraints in backend generation.
arXiv Detail & Related papers (2026-05-07T15:44:40Z) - LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z) - Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
arXiv Detail & Related papers (2026-04-21T15:05:58Z) - PSR2: A Phase-based Semantic Reasoning Framework for Atomicity Violation Detection via Contract Refinement [9.36682660657815]
PSRtextsuperscript2 is a novel collaborative static analysis framework that integrates structural path searching with deterministic semantic reasoning.<n>Experiments on 1,600 contract samples demonstrate that PSRtextsuperscript2 significantly outperforms pattern-matching baselines.<n>Our fusion logic effectively reduces the false-positive rate by nearly half compared to single module analysis.
arXiv Detail & Related papers (2026-04-08T11:46:01Z) - AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
arXiv Detail & Related papers (2026-03-15T16:13:58Z) - SUNSET -- A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation [2.8210523386413087]
We present SUNSET, a ROS2-based exemplar that enables rigorous, repeatable evaluation of architecture-based self-adaptation.<n> SUNSET includes the segmentation pipeline, a trained ML model, uncertainty-injection scripts, a baseline controller, and step-by-step integration and evaluation documentation.
arXiv Detail & Related papers (2026-01-20T08:40:57Z) - Detecting Pipeline Failures through Fine-Grained Analysis of Web Agents [0.48156730450374763]
This work analyzes existing benchmarks and highlights the lack of fine-grained diagnostic tools.<n>We propose a modular evaluation framework that decomposes agent pipelines into interpretable stages for detailed error analysis.
arXiv Detail & Related papers (2025-09-17T19:34:49Z) - Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems [20.846301581161978]
Failure attribution in multi-agent systems is a critical yet unsolved challenge.<n>Current methods treat this as a pattern recognition task over long conversation logs.<n>A2P Scaffolding transforms failure attribution from pattern recognition into a structured causal inference task.
arXiv Detail & Related papers (2025-09-12T16:51:15Z) - How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $τ$-bench [58.114899897566964]
In a multi-turn conversational environment, large language models (LLMs) often struggle with consistent reasoning and adherence to domain-specific policies.<n>We propose the Input-Reformulation Multi-Agent (IRMA) framework, which automatically reformulates user queries augmented with relevant domain rules.<n>IRMA significantly outperforms ReAct, Function Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively.
arXiv Detail & Related papers (2025-08-28T15:57:33Z) - Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
arXiv Detail & Related papers (2025-03-31T07:31:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.