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software testing

2,998 papers

#software testing Open access Oct 2026

meshio++: Tools for mesh files

What's Changed Terminal rendering, second release: field rendering and every language surface (v16.34.0, ABI 24) by @loumalouomega in https://github.com/loumalouomega/meshioplusplus/pull/139 Fluent reader: fix two integer overflows found by the fuzz campaign by @loumalouomega in https://github.com/loumalouomega/meshiop...

Vicente Mataix Ferrándiz, Nico Schlömer, Mariam Kesba et al. · 0 citations
#software testing Open access Oct 2026

PyAges v1.0: an extensible Python toolkit for inferring groundwater transit-time distributions from environmental tracers

Abstract. Lumped-parameter models infer groundwater transit-time distributions by combining environmental-tracer input histories with parameterized age distributions. Their practical use requires flexible tracer representation, reliable repeated numerical integration, and explicit treatment of uncertainty when differen...

Jean‐Raynald de Dreuzy, Sarah Leray, Jean Marçais · 0 citations

Automated Validation of Service-Oriented and API-Based Enterprise Systems for Reliable Digital Transformation in Saudi Arabia

The extent to which Saudi Arabia is undergoing a digital transformation now relies on enterprise platforms that are put together using APIs, service-oriented components, microservices, identity services, data stores, and capabilities that are managed externally. This architectural modularity speeds up the delivery proc...

Hossam Kenawy · 0 citations
#software testing Open access Oct 2026

Lost Data Retrieval Lab: A Safe and Reproducible Workflow for Simulated File Recovery and SHA-256 Integrity Verification

Lost Data Retrieval Lab is a Python-based educational application designed to demonstrate simulated file recovery, digital evidence documentation, and data integrity verification. Built with Python, Streamlit, and Pandas, the application generates synthetic test files, records SHA-256 hashes, simulates accidental delet...

Abeeha Zeeshan · 0 citations
#software testing Open access Oct 2026

Design and Analysis of a Hexagonal Patch Antenna with Integrated Metasurface for Satellite Applications

In this paper, the design, simulation, and analysis of a novel hexagonal patch antenna operating at a frequency of 10.5 GHz are proposed. The hexagonal shape of the patch is chosen for its unique geometric properties, providing improved radiation characteristics and enhanced bandwidth. The proposed antenna leverages sl...

V. Gnanalakshmi, S. Asha, G. Sowmiyadevi · 0 citations
#software testing Dataset Open access Oct 2026

JGrijalba/Data used for the automatic classification of the physical composition of the urban soundscape

This repository documents a manuscript on urban soundscape composition. It contains supplementary materials, supporting data used to train and deploy a convolutional neural network (CNN) for soundscape classification in Venice, Italy (U-VNE-CNN), and data required to reproduce the results presented in the manuscript. T...

Julián Grijalba, Carlos A. Durán Paredes · 0 citations
#software testing Open access Oct 2026

Binomial MLMC: code artifact

Software and hardware sources for multilevel Monte Carlo (MLMC) pricing of an arithmetic-average Asian call with Binomial MLMC: exact Binomial-count increments on a Cox–Ross–Rubinstein lattice, drawn from alias tables, with prices read from a ROM, so that the per-step kernel needs no multiplier.Contents: - Python MLMC...

Pieter Fabry · 0 citations
#software testing Open access Oct 2026

ASSURANCE ENGINEERING FOR AUTONOMOUS QUANTUM–NANO HEALTH INFRASTRUCTURE AT THE LIMIT Measurement Trust × Clinical Authority × Recoverable Infrastructure This exploratory research monograph investigates how safety evidence could be constructed for future autonomous quantum–nano health infrastructure without confusing physical sensing accuracy, AI recommendations, professional permission, clinical effectiveness, and regulatory authorization. The central research question is whether every consequential recommendation can be reconstructed from traceable measurements, interpreted with declared uncertainty, subjected to legally and professionally valid human authorization, and linked to an independently verified safe-state and recovery pathway. The monograph is a tri-coupled synthesis of three prior mother architectures: Swarm Manufacturing and Robotic Matter at the Limit, particularly chapters 72–79, for distributed-agent security and operational fault tolerance; Autonomous Quantum Health Infrastructure at the Limit, particularly chapters 25–82, for bounded decision authority, recoverable intervention architecture and successor-ready safety evidence; and Self-Calibrating Quantum Biosensing Networks at the Limit, particularly chapters 65–73, for reference trust, bounded calibration, serviceability and governance. These mother architectures supply research methods and constraints. Their combination does not demonstrate that the integrated technology is clinically effective, medically approved, or already deployed. Across 82 chapters and 12 separately paginated research studies per chapter, the work develops a versioned Claim–Argument–Evidence architecture for non-clinical quantum–nano health systems. It examines raw-to-result metrology, shared-reference errors, uncertainty propagation, model calibration and digital twins, operator authentication, explicit professional authority envelopes, fail-closed control, verification, manual override, classical backup, malicious or degraded devices, alarm fatigue, incident replay, recovery closure, lifecycle economics, fairness, privacy and independent cross-site reconstruction. Thirty physical and mathematical derivation plates separate established relations and their validity conditions from the book's unvalidated research heuristics. The NQ-14 deployment expression is a conceptual four-gate test: adequate evidence, valid authority, verified recovery and compliance with the applicable regulatory pathway are jointly necessary. Its Boolean structure is not a proof of safety or legal status, nor can a composite index replace consequence-specific vetoes. Every chapter offers a candidate model and a three-dimensional evidence geometry. The intended verification environment uses synthetic observations and hardware-in-the-loop non-clinical simulations. Proposed trials inject inaccurate calibration references, correlated sensor faults, operator overload, unauthorized delegation, model and firmware drift, unavailable classical backup and simultaneous communication failures. Outcomes are compared with strong classical-only and human-supervised baselines under a declared test domain; unauthorized high-consequence action, missing provenance or unrecoverable state is a rejection condition even when prediction accuracy rises. The volume concludes with 200 independent Research Gates and 33 provisional Phoenix Answer Embryos. Each has an equation egg drawn from an existing physical or mathematical relationship and accompanied by a validity boundary. An accompanying Research Gates spreadsheet preserves gate questions, tests, vetoes, provenance, assurance-case claims, human authority matrices and recovery evidence so that later laboratories may reproduce, refute, revise, split or retire the proposals. SCIENTIFIC, CLINICAL AND REGULATORY BOUNDARY: This is an exploratory, non-clinical, partly template-assisted research architecture. It reports no clinical trials, medical outcomes, validation of autonomous quantum–nano therapy or regulatory clearance. AI models do not obtain permission to diagnose or treat patients through inference quality. Clinical uses would require qualified professional oversight, intended-use evidence, independent validation, ethical governance and applicable jurisdiction-specific regulatory processes. Relevant context includes BIPM/JCGM metrology guidance, IMDRF SaMD clinical evaluation documents and FDA guidance on AI device software change management, none of which is presented as approval of the proposed platform. The thirty derivation plates are research-level derivation sketches rather than complete textbook proofs; the 233 numbered equation eggs are not discoveries of 233 new physical laws. Feng Cheng-en (33) × Starli STARLI Nano–Quantum Tri-Coupling Research Series | Flagship 14 | 2026

ASSURANCE ENGINEERING FOR AUTONOMOUS QUANTUM–NANO HEALTH INFRASTRUCTURE AT THE LIMIT Measurement Trust × Clinical Authority × Recoverable Infrastructure This exploratory research monograph investigates how safety evidence could be constructed for future autonomous quantum–nano health infrastructure without confusing ph...

33 · 0 citations
#software testing Dataset Open access Oct 2026

MESA 5.7.0 synthetic test data packages for sensitivity mapping

Description: Four synthetic data packages for testing MESA 5.7.0, the open-source tool for mapping environmental sensitivity. Each ZIP is one MESA project (config.ini plus input/ data), restored via Manage data → Restore data. The packages contain inputs only, no computed results: the recipient runs Process and gets th...

Ragnvald Larsen · 0 citations
#software testing Open access Oct 2026

27) Crypto-Agile Post-Quantum Security Architecture for U.S. Critical Infrastructure Optical and IP/MPLS Networks

AbstractObjective. Public-key cryptography protects key establishment, authentication, software integrity and trust relationships across the optical transport and IP/MPLS networks that carry U.S. critical-infrastructure communications. A sufficiently capable quantum computer would break the widely deployed algorithms o...

M. Rizwan Yasin · 0 citations
#software testing Open access Oct 2026

AI Delivery Risk Intelligence: E-commerce Delivery Risk Ranking and Uncertainty Analysis

AI Delivery Risk Intelligence is a research software prototype for ranking delivery-delay risk from historical e-commerce order data. This v0.1.0 source-code release includes an XGBoost pipeline, chronological cohort selection, purchase-time feature engineering, calibration diagnostics, bootstrap intervals for evaluati...

Zohreh Kaheh · 0 citations
#software testing Open access Oct 2026

Adaptation and Retention of Digitally Fabricated Presurgical Orthopedic Plates for Infants with Cleft Lip and Palate

Objectives: This study aimed to evaluate the fitness and retention of presurgical infant orthopedic (PSIO) plates fabricated from three materials—polyethylene terephthalate glycol-modified (PETG), PETG/polyurethane (PETG/TPU), and direct-printed Tera Harz resin—within a standardized in vitro framework. Methods: Thirty...

F. Shakir, Mushriq F. Abid · 0 citations

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Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

GPT-Lab Sep 23, 2026

Requirements Don’t Live in Isolation: What We’re Exploring with Req-Space

Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

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