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 38
Actionable Meta-Requirements for AI Robustness Auditing
AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external dependencies).ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, spatial validation, governance, provenance…DICE: Tilting the Odds through Interface Attackson System One Model
System One models offer typed judgments that applications can turn into decisions.SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals.Following Breadcrumbs in Code: What Accidentally Committed Ad-Hoc Logs Reveal about Developer Comprehension
Developers frequently insert temporary print or log statements, known as **ad-hoc logs**, to better understand program behavior at runtime, particularly when facing unexpected issues or complex…IRONPROOF: COBOL-to-Python Transpilation with SMT-Based Equivalence Checking
Translating COBOL to a modern language can change what a program computes, and LLM translations carry no guarantee of equivalence.Skill Constellations: Tracing the Supply Chain of Agent Skills on GitHub
Agent skills are SKILL.md instructions and scripts that AI coding agents such as Claude Code and Codex run with the permissions of their user.Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests
AI coding agents are moving from local code assistance into pull-based workflows, where generated contributions must be reviewed, explained, and maintained within existing project norms.Retromorphic Testing of Quantum Compiler Passes
Quantum compilers play a critical role in transforming high-level quantum programs into optimized, hardware-compatible circuits.Characterizing Overconfident Failure in LLM-Based Code Generation
Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based correctness checks.Closed-loop evaluation of LLM agents for embedded software development
Large language models (LLMs) are increasingly deployed as coding agents that edit files, run builds and tests, inspect execution results, and repair software iteratively.Evaluating Local Language Model Agents for Reproducible Data Engineering: An Empirical Software Engineering Study of Mobility Workflows
Large language model (LLM) agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable open-weight agents remains limited.SSCBench: Evaluating the Evidential Validity of Fault-Injection Tests for Tool-Using LLM Agents
Fault injection is increasingly used to evaluate the reliability of tool-using LLM agents.SoK: Are LLMs Reliable at Source Code Recovery? A Taxonomy and Empirical Evaluation
Effective source recovery is critical to security applications such as malware analysis, vulnerability assessment, and legacy maintenance.SWE-Journey: Towards More Realistic Evaluation of Coding Assistants through Long-Horizon, Multi-Turn Interaction
Coding assistants such as Claude Code and Codex have become a major application of LLM agents, yet existing benchmarks remain far from real-world use, particularly in task horizon and interaction…Chronos Enables Code Agents to Reason over Software Evolution
Historical pull requests record the design decisions, compatibility constraints, and implementation patterns behind a codebase's current state.Runnable Commit Untangling for Coding Agents
Coding agents produce large, tangled patches that mix multiple development purposes, making the code hard to review and maintain.Where Do the Tokens Go? Understanding and Reducing Costs in LLM Agents for Vulnerability Discovery
LLM agents can spend millions of tokens during vulnerability discovery without producing a working proof of concept (PoC).PolyCodeEval: Benchmarking Multilingual Code Generation from Functions to Repositories
As large language models increasingly move toward repository-level software engineering, existing code-generation benchmarks remain fragmented across language coverage, task granularity, and…One Skill Too Many: How Co-Installed Skills Conflict in Coding Agents
Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task.