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 9
Do Coding Agents Catch Their Own Bugs? (They Don't.)
In software development, engineers need to foresee and fix potential problems with their code that are not explicitly specified in requirements---such as pre-emptively considering vulnerabilities or…A Domain Driven Design Based Reference Architecture for IoT Systems Engineering
The Internet of Things (IoT) paradigm continues to gain significant traction across diverse do-mains, primarily driven by its transformative and socio-economic benefits.AdapBench: Adaptive Early-Stopping and Prioritization for Efficient Microbenchmarking in Cloud-based CI/CD
Cloud-based software systems frequently release new versions, a fact further accelerated through agentic coding.Toward Bounding Coding-Agent Autonomy in Software Maintenance: A Pilot Study of Boundary Contracts
As coding agents gain greater autonomy to modify software repositories, relevant context alone does not define their operational authority.Causality-Based Fault Localization
We introduce Causality-Based Fault Localization (CBFL), a novel approach to fault localization grounded in the theory of actual causality.ProofPatch: Reviewing Lean Statements and Keeping the Reviews Valid as Formalizations Evolve
The kernel of the Lean proof assistant checks that a proof establishes its statement.WFDB-x: Conformance-Tested Implementations of the WFDB Software Package in Seven Programming Languages
The WFDB format is widely used to store and exchange physiological waveforms and annotations.AIFaultBench: A Reproducible Benchmark of Real-World Faults in AI Software Systems
AI-based software systems remain difficult to debug because their failures can involve interactions among code, models, data, dependencies, hardware, and execution environments.RepGen: Reproducing Deep Learning Bugs through Context-Aware Code Generation and Verification
Reproducing deep learning (DL) bugs is challenging because failures can involve source code, models, data, dependencies, and execution environments.One Verdict Is Not Enough: Regression Testing Learned World Models and Their Test Harnesses
Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases.Lineage Is Memory: Parent Inheritance and Feedback Disclosure in Short-Budget LLM-Guided Optimization
Iterative language-model optimizers refine prompts and programs by evaluating a candidate, showing a proposer model a sample of failing cases, and keeping revisions that improve performance.ROSTrans: Empirically Benchmarking LLMs for ROS1-to-ROS2 Migration
Although large language models have shown strong capabilities in domain-specific code generation, their performance in robotics software development, especially for the Robot Operating System (ROS)…Cognitive Inclusivity in AI Coding Assistants: Evidence from GitHub Copilot
AI coding assistants are commonly used for daily software development tasks.Yesterday's Code, Today's Dependencies: How Dependency Selection Shapes PHP Migration Benchmarks
Migration benchmarks need projects whose tests pass before migration begins.Architectural Debt Enforcement under Incomplete Observation: A Controlled Study of Ratchets and Evidence Qualification
Architectural debt gates must distinguish an allowed change from an incompletely observed one.What Does a Repair Agent Gain from Remembering Its Failure? An Exploratory Study on Real Python Bugs
When a coding agent fails a repair task, it is often retried with a record of the failure: the transcript, a self-written lesson, or extracted test signals.Post-SQLFix: Property-Guided Repair of LLM-Generated SQL with Runtime Verification
Large Language Models (LLMs) for Text-to-SQL frequently generate queries with syntax errors, invalid identifier references, or violations of SQL scoping and typing rules.SCRBench: Beyond Equivalence Checking for Semantic Change Reasoning in Code
Understanding how code changes affect program semantics is a core aspect of code reasoning in language models.Prompt Less, Infer More: Static Analysis-Guided LLM Type Annotation for Python
Type annotations improve the maintainability and reliability of Python programs, but many real-world projects remain under-annotated because manual annotation is time-consuming and error-prone.What Carries Architectural Signal? A Code-to-Architecture Mapping Study
Code-to-architecture mapping assigns source files to modules of a reference architecture.