SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
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Trae agent: An llm-based agent for software engineering with test-time scaling
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SWE-WebDevBench finds that AI app builders commonly fail at translating business needs into complete, secure, production-ready software due to specification bottlenecks, frontend-backend decoupling, low engineering quality, and security weaknesses.
A custom LLM agent achieves 94% manually verified success on a new benchmark of 35 software analysis setups, outperforming baselines at 77%, but struggles with stage mixing, error localization, and overestimating its own success.
Presents a component-centric PoC dataset of malicious MCP servers and a two-stage behavioral deviation detector Connor achieving 94.6% F1-score.
DBCooker automates synthesis of database native functions via LLM-guided characterization, coding plans, hybrid filling, and progressive validation, delivering 34.55% higher accuracy than baselines on SQLite, PostgreSQL, and DuckDB while generating functions absent from SQLite 3.50.
The first empirical study of test overfitting shows that auto-generated tests from issues can lead to code that passes observed tests but misses important cases or breaks functionality in SWE-bench issue resolution.
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
LLVM-Bench supplies 423 validated LLVM issues and LLVM-Gym automates evaluation, showing LLMs are limited but an ensemble reaches 21.99% resolution.
CLIP proposes a cosine-law-based pruning method for IVF vector search enabling O(1) cluster and log-time vector pruning with guarantees, plus variants for hierarchical and dynamic settings, showing up to 78% pruning and 69% efficiency gains.
REC RL improves LLM code generation by automatically assessing and optimizing requirement difficulty with adaptive curriculum sampling, yielding 1.23-5.62% Pass@1 gains over baselines.
LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
More fault localization context does not consistently improve LLM-based program repair; file-level context gives 15-17x gains, optimal around 6-10 files, while line-level context often degrades performance from noise.
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
TestPrune minimizes regression test suites to improve bug reproduction and patch validation in LLM-based agentic repair pipelines, delivering 6-13% relative gains on SWE-Bench benchmarks at low API cost.
CP-Agent improves LLM competitive programming performance via calibrated feedback mechanisms that target false-admission risk, evidence against bad programs, and success hazard.
Empirical study finds coding agents produce fewer and less intense tangled refactorings than humans on Multi-SWE-bench; a refactoring-aware refinement improves compilability from 19.34% to 38.33% and resolves 2.79% more issues.
The paper introduces KISS Sorcar, a simple open-source AI agent framework with a five-layer hierarchy and git worktree isolation to address context limits, error propagation, and reviewability in software engineering tasks.
Agent-generated tests mainly act as observational feedback channels and do not meaningfully improve issue resolution success in current LLM software engineering agents.
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