Applied & Human-Centered · Computer Science & AI
Software Engineering
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1,466 papers, from 31 August 2026 to 5 November 2026: 1,451 arXiv preprints and 15 OpenAlex records.
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Newest papers, page 12
Precedent or Intent? What Coding Agents Follow When Evolving Existing Code
Existing code reveals past implementation decisions but rarely explains the reasoning behind them.LASSO: An LLM-Agent Synthesized System for SQL Validation
The rise of Large Language Models (LLMs) has significantly advanced text-to-SQL capabilities, but generated queries may have errors.Detecting Incorrect LLM Explanations for Automated Program Repair
LLM-generated explanations help developers understand automated program repair (APR) rationale and assess patch quality.DIFUZZI: A Differential Fuzzing Tool for Code Functional Similarity Assessment
A range of software engineering tasks, such as optimization and refactoring, seek to preserve the behavior of software components and require methods to assess functional similarity.RoleFUZZER: User-Behaviour Fuzzing for Role-Specification Adherence in Large Language Models
Large language models (LLMs) are often conditioned into specific roles such as tutors, interviewers, or agents acting for an organisation, through system prompts that specify how they should behave.Security Impact of Coding Agent's Memory Files
Coding agents automatically load project memory files such as CLAUDE.md at the start of every session and treat them as trusted project guidance.An Alignment Benchmark for Enterprise Agents on Era by Eon
AI agents are widely used in enterprises to access business systems and act on them in pursuit of a goal.Repairs for Whom? Evaluator-Executor Mismatch in Selecting Repairs for Failed LLM Agents
When an LLM agent fails, repair methods choose which action to replace and what to put in its place.How Deterministic SAST and AI Security Agents Divide the Work: Coverage, Harness Policy and Cost in Snyk VulnBench 2.0
We ask what AI coding agents and AI security harnesses find that deterministic static application security testing (SAST) does not, and what they miss that SAST catches.When LLM Architectures Meet Smart Contract Vulnerability Detection: a Replication Study
Smart contracts are immutable programs deployed on blockchain platforms, where vulnerabilities can translate directly into financial loss.The Reality Gap in Automated Driving Systems Testing: Landscape, Challenges, and Research Directions
Simulation is essential for testing automated driving systems (ADS), but discrepancies between simulation and reality can undermine testing conclusions.Write Once, Instrument Anywhere: Portable, Sandboxed Tooling with WebAssembly Components
Monitoring and debugging deployed software is crucial for scalability, performance, and robustness.CARI: Cross-Boundary Adversarial Runtime Injection in Coding-Agent Workflows
Coding agents increasingly develop, test, and integrate software whose components are owned by different developers, teams, or services.When Relevant Code Is Wrong: Runtime-Aware Context for AI Coding Agents
AI coding agents increasingly rely on retrieved code and development history to guide software changes.Beyond Passing Tests: A Systematic Review and Controlled Re-evaluation of Techniques for Improving the Quality of LLM-Generated Code
Large language models pass most functional-correctness benchmarks, but passing tests says little about whether their code is readable, efficient or secure.Beyond atol and rtol: Sound Test Oracles for Low-Precision GPU Kernels
Low-precision GPU kernels are commonly tested against a reference using hand-picked absolute and relative tolerances.DeepDiff: Neuro-Dynamic Regression-Freedom Checking for Security Patch Validation
Automatically validating AI-generated security patches remains a significant challenge.ProCerDCL: Query-Efficient Black-Box Probabilistic Verification of Deep Learning Compiler Optimization
We present \tool, a query-efficient framework for black-box probabilistic verification.From Ripple to Boundary: Deterministic Impact Boundaries for LLM-Based Software Evolution
The landscape of software evolution is undergoing a fundamental paradigm shift in the era of Large Language Models.CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents
Tool-using language agents increasingly modify external systems, yet most runtime safeguards allocate scrutiny by generic uncertainty scores or coarse mutation heuristics.