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 18
Software Engineering for AI-Driven Science
AI has made significant contributions to scientific discovery.NovaFabric: A Black-Box Recorder for AI Agents with Sealed, Replayable, Diffable and Auditable Runs
An AI agent run is no longer a function call.Do AI Coding Agents Write Infrastructure as Code Like Humans? An Empirical Study of GitHub Pull Requests
Infrastructure-as-Code (IaC) configurations define cloud resources, networking, and access control, and AI coding agents increasingly write them.Built to Remember, Shipped to Forget: Operational Contracts, Failures, and Repairs in Containerized LLM Agent Deployments
LLM agent systems reach production as container images run by platforms such as Docker, Compose, and Kubernetes.CodeMeepo: A Multi-Agent Workflow for Repository-Level Code Migration and Optimization
Repository-level code migration rewrites an entire project in a new programming language while preserving its observable behavior.AgentProofBench: Benchmarking Reusable Verifiers for Web Agents
Reusable Web-agent verification requires a rule to follow changing requirements and support subsequent execution.PMU Foundation Model Bridge: A Tool for Connecting Synchrophasor Streams to Time Series Foundation Models
This paper presents PMU Foundation Model Bridge, an open source tool that connects online phasor measurement unit (PMU) or phasor data concentrator (PDC) streams to publicly available time series…OneCodeReviewBench: Typed Decisions for Code Review and Search
Code review requires finding functional bugs, memory-safety errors, security weaknesses, and performance problems across languages and software stacks.BoardSafety: AI-Ready Data from Manufacturer Documentation for Development-Board Safety
The development of large language models (LLMs) are capable to assist with hardware experiments.Secure and Functional Code Generation with Self-Evaluation
Large Language Models (LLMs) can generate functionally correct code that remains insecure.Can Agentic Code Review Catch Vulnerabilities Before They Are Merged?
LLM agents have been increasingly adopted for security-focused code review, but existing benchmarks mostly derive the reviewed changes from their later fixes instead of the real-world…Developers as Managers of AI Agents: A Workplace Shadowing Study
As AI coding agents become more autonomous, developers increasingly direct, coordinate, and check their work.Build or Adopt? Custom-Built vs. General-Purpose LLM Agent Harnesses for Automotive GUI Testing
As general-purpose LLM agent harnesses mature, organizations must decide whether to continue developing in-house systems or adopt existing alternatives.SWE-Ladder: Learning to Localize, Reproduce, and Patch via Progressive Privileged Self-Distillation
Resolving a repository-level issue typically requires a software-engineering (SWE) agent to localize the faulty code, reproduce the failure, and patch the code.Covered Is Not Checked: Which Branch Outcomes Would Your Tests Notice?
Code coverage reports what tests execute, not whether tests detect faults.VulDec: Vulnerability Detection in Binaries via Neural Decompilation
Automated vulnerability detection in binary executables is challenging due to the lack of source code and the limited semantic information available at the assembly level.Software Engineering LLMs: Why We Need Them and How Software Engineering Helps Build Them
Software engineering (SE) expertise can shape how coding large language models (LLMs) are trained and which engineering capabilities they acquire.ArchGuard-APR: Architecture-Constraint-Aware Multi-Agent Program Repair Using Repository Graphs and Execution Feedback
Repository-level automated program repair must reason about more than a localized failing statement.Numina Fuse: Interactive Autoformalization in Lean 4
Numina Fuse is an agent-native application for formalizing mathematics in Lean 4.FlashScript: Compiling Recurring Agent Workflows into Reusable Neuro-Symbolic Programs
Many agent tasks recur with new data or small changes, yet agents regenerate their reasoning and actions on each run, producing different results.