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software testing

3,195 papers

#software testing Dataset Open access Oct 2026

Data and code for "Historical introduction pools pre-structure future plant invasion debt under climate change"

This repository provides the compiled analysis inputs, R code, figure source data and GIS rasters supporting the manuscript “Historical introduction pools pre-structure future plant invasion debt under climate change”. Version 2.0 contains the final USA-intersection climatic-analogue reconstruction and its correspondin...

Silun Chen, Renwu Wu, Yuxin Qiao et al. · 0 citations
#software testing Open access Oct 2026

kadubon/collective-intelligence-overlay: v0.4.3: bounded near-transfer sensitivity pilot

v0.4.3 adds a bounded, preregistered near-transfer sensitivity pilot and offline resource endpoints. Its 86 package implementation files remain byte-for-byte identical to the published 0.4.2 runtime used in the study; distribution metadata selects 0.4.3. The real CPU Gemma4:e4b pilot completed 54 requests and 26,256 me...

K. Takahashi (Kadubon) · 0 citations
#software testing Dataset Open access Oct 2026

PHASE-MOO: reproducibility data and code for calibrated geometric population optimization

Data and source code supporting PHASE-MOO: population search over competing local geometries for Pareto front approximation (version 2.0.0, revised implementation and experiments). This version replaces the experimental evidence of version 1.0.0, which remains available as the historical record. It contains the experim...

Marco P. Rodrigues, Rafaela M.J. Lemos · 0 citations
#software testing Open access Oct 2026

SeraEdit: Scoped and Validated Language-Guided MusicXML Editing (SoftwareX R1 source and evidence)

SeraEdit is local-first research software for scoped, source-bound MusicXML editing through ScorePatch transactions, deterministic validation and reversible execution. This SoftwareX R1 revision corrects fixture reproducibility and source-archive completeness, and includes saved DeepSeek three-condition outputs, a 200-...

Yuan Gao · 0 citations
#software testing Open access Oct 2026

Σ-AFO/HM: v2.0 Bounded Adaptive Routing with Human Authority Gates

Documentation-only v2.0 methodological note in the existing Σ-AFO/HM version family. This original English PDF derives a bounded adaptive-routing rule in which human authority, evidence and risk are mandatory gates rather than compensable scores. Nineteen local finite synthetic tests passed. They do not establish gener...

Riccardo Giudici · 0 citations
#software testing Open access Oct 2026

Modified Stockwell Frequency-Wavenumber Workflow for Surface-Wave Dispersion Representation

# MSFK MATLAB implementation and reproducible validation This package accompanies **Modified Stockwell Frequency-Wavenumber Workflow for Surface-Wave Dispersion Representation** by Hanbing Ai, Jiangtao Li, Yunus Levent Ekinci, Yang Hu, Ling Ning, and Yingwei Yan. The MSFK algorithm was developed by **Hanbing Ai**. It s...

Hanbing Ai, Jiangtao Li, Yunus Levent Ekinci et al. · 0 citations
#software testing Open access Oct 2026

COSPEC Comparison Harness and Evaluation Dataset

An automated comparison harness and the complete 150-trial dataset supporting the evaluation of COSPEC, Spec-Driven Development using GitHub Spec Kit, and vibe coding. The dataset contains five conditions, two cross-model Maker/Director pairs (Claude and Codex), three Maker reasoning-effort levels, and five repetitions...

Yongjin Seo · 0 citations
#software testing Open access Oct 2026

Enhancing security in LLM applications: a performance evaluation of early detection systems

Abstract Prompt injection (PI) attacks threaten novel software applications, which have LLM-based functionality. Prompt leakage, a variant of prompt injection, constitutes a serious confidentiality risk to those applications. Existing defenses against PI attacks currently cannot identify prompt leaks precisely. Moreove...

Valerii Gakh, Hayretdin Bahşi · 0 citations
#software testing Open access Oct 2026

DRIFT: A Gate Framework for Target-Relative Inference Under Model Uncertainty

DRIFT ist ein vorgeschlagenes, zielrelatives Entscheidungsframework zur Beurteilung, ob eine statistische Inferenz für eine konkrete Zielaussage unter den vorgegebenen Annahmen und der verfügbaren Evidenz hinreichend unterstützt ist. Das Framework integriert fünf getrennte Prüfbereiche: Matching (M), Observability (O),...

Bernd von Mallinckrodt · 0 citations
#software testing Open access Oct 2026

DRIFT: A Gate Framework for Target-Relative Inference Under Model Uncertainty

DRIFT ist ein vorgeschlagenes, zielrelatives Entscheidungsframework zur Beurteilung, ob eine statistische Inferenz für eine konkrete Zielaussage unter den vorgegebenen Annahmen und der verfügbaren Evidenz hinreichend unterstützt ist. Das Framework integriert fünf getrennte Prüfbereiche: Matching (M), Observability (O),...

Bernd von Mallinckrodt · 0 citations
#data science Open access Oct 2026

Stats Toolkit: free statistical analysis tools for research and teaching

StatsToolkit is a free, privacy-first web application that helps students, researchers, and educators make sound statistical decisions without installing any software. It runs entirely in the browser: any data a user provides is processed locally on their own device and is never uploaded or stored. It provides five too...

Andrew J. Callaway · 0 citations
#edge computing Open access Oct 2026

Phantom Vision Lab: a computational instrument for perturbing an image-analysis pipeline and measuring how its output changes

# Phantom Vision Lab — a computational instrument for perturbing an image-analysis pipeline and measuring how its output changes **A deterministic structured-light stimulus generator paired with a fixed classical computer-vision pipeline, used to measure how much the pipeline's own output moves when 11 computational pa...

Phantom Vision Lab contributors · 0 citations

From tech blogs

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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.

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