Applied & Human-Centered · Computer Science & AI
Software Engineering
The newest papers in Software Engineering that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
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 21
Governance-State Corruption in Long-Term Memory for LLM Agents: A Cross-Model Stress Test
An LLM agent can retrieve a memory correctly yet use it incorrectly after its validity state changes.From Tokens to Credits to Outcomes: Pricing AI Across Models, Applications, and Cloud Platforms
Between 2023 and 2026, AI changed how software is priced.From Natural Language to Verified C: Verification-Guided Generation with ACSL Specifications
AI-assisted code generation amplifies the demand for automated formal verification.Can Loan Processors and Underwriters Spot AI-Edited Statements? An Empirical Study
AI-assisted editing can produce loan documents that preserve familiar issuer layouts while changing the applicant's identity.Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training
Scaling GUI reinforcement learning requires environments that preserve real-app behavior.VLTest: Finding Joint Image-Text Failures in Vision-Language Models
Vision-language models (VLMs) are increasingly embedded in software systems.What Ethereum Developers Ask For, and Whether They Get It: Ten Years of Ethereum Stack Exchange
The Ethereum community learns where developers struggle through surveys and interviews, which record what developers say when asked about a chosen theme.Folding Pooled Usage Examples into the Tool Index Lowers Retrieval of Unused Tools
AI agents often find tools by searching their documentation.ATLAS: A Multi-Agent Framework for Lifting Binary to Canonical Compiler IR
Lifting stripped, heavily optimized binaries to a canonical, pre-optimization LLVM intermediate representation (IR) is essential for downstream security analysis and legacy software migration when…When Code Gets Cheap, Evaluation Gets Scarce: An Experience Report on AI-Assisted Agile Software Engineering
Coding assistants are widely reported to speed up code production; most accounts stop there.REGATTA: A Cross-Domain Measurement Framework and Fault-Injection Harness for Enterprise AI Agents
Enterprise AI-agent evaluation needs evidence about whether test suites expose specified faults and whether their reference rules are trustworthy.Tracing Hallucinations in LLM Reasoning for UML Class Diagram Generation: An Empirical Taxonomy and Mitigation Study
Large Language Models (LLMs) can generate UML class diagrams from natural language requirements, but hallucinations may compromise the correctness and completeness of the resulting diagrams.ProductWebBench: Evaluating Website Continuity Beyond Task Completion
Frontend coding agents are usually graded on one question: did the requested target get built.Beyond Accuracy: Disentangling Decomposition from Typed-State Quality Assurance in Multi-Agent LLM Pipelines
Multi-agent LLM pipelines are often credited with gains in software engineering tasks, but it is rarely clear whether a gain stems from the typed contract between agents or from the specialized…Superintelligent Software Engineering: Governance That Scales with Capability
Software agents are being trained to exceed human engineers; we argue that capability alone does not produce better software engineering.Typed Template Fuzzing
Fuzzing software with complex inputs has been a longstanding challenge.Automating Feature Flag Cleanup at Scale with Multi-Agent LLM Systems: An Industrial Experience at DoorDash
Feature flags are runtime configuration mechanisms used for gradual feature rollouts, experimentation, and kill switches.ConQEC: Concolic Testing for Quantum Error-Correcting Code Implementations
Quantum error-correcting code (QECC) implementations are hybrid software-control systems: measured syndromes drive decoder dispatch, recovery actions, and logical checks.Procrustes-Bering-Sheaft: Automated Model Discovery and Virtual Crash Tests for Microservices
Procrustes-Bering-Sheaft automates lightweight model discovery and virtual crash tests for microservices.Adaptive Skill Granularity for Coding Agents: Runtime Routing and Macro-to-Micro Recovery
Reusable skills allow large language model agents to package task-specific procedures, tool affordances, and execution guidance into modular units.