software testing

A Fully Automated, Deployment-Aware Testing Pipeline for IoT-Based Automotive Applications

This work presents an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications that combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency.

Auditing and Decomposing Feedback-Driven Evolution in LLM Test Generation under the Oracle Problem

An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.

From design to verification: applying the software quality and test plans to the SST telescope control system for CTA

Design of operational software for infrared test system

Automated verification of the ARIEL instrument control unit boot software using TCL-based test sequences

ICU to TCU interface emulator for the ARIEL Telescope Assembly thermal-vacuum test: software and graphical user interface

The application software for the instrument control unit of the Ariel mission: design and testing approach

The MICADO first light imager for the ELT: cryostat control software tests and lessons learned

Контроль достоверности многоканальной телеметрии робототехнического манипулятора на основе графа межканальной согласованности

Работа посвящена контролю качества данных в многоканальной телеметрии робототехнического манипулятора. Цель исследования – разработать и экспериментально оценить метод, который выявляет и локализует искаженный канал без обучения нейронной сети и без физического стенда. Метод объединяет показатели полноты, временной корректности и стабильности каждого потока с кинематическими и динамическими невязками между энкодерами, токами приводов, инерциальным каналом и внешней оценкой положения рабочего органа. Пороговые значения вычисляются по чистому калибровочному интервалу с использованием медианы и медианного абсолютного отклонения; локализация выполняется по графу межканальных связей. Воспроизводимый программный эксперимент включает 864 сценария, шесть видов искажений, четыре канала, три уровня интенсивности и двенадцать повторов. Предлагаемый метод достиг среднего F1 0,862 при 95-процентном бутстреп-интервале [0,845; 0,879], тогда как фиксированные правила и адаптивный одноканальный контроль дали 0,622 и 0,791 соответственно. Средняя точность локализации составила 0,843, доля ложных срабатываний – 0,0059. Наибольший выигрыш получен для постоянных смещений; для выбросов и замирания одноканальные методы оказались конкурентоспособнее. Результат представляет собой интерпретируемый модуль контроля входной телеметрии, пригодный для встраивания перед хранением и аналитической обработкой данных. The paper addresses data quality control in the multichannel telemetry of a robotic manipulator. The aim of the study is to develop and experimentally evaluate a method that detects and localises a corrupted channel without training a neural network and without a physical test bench. The method combines indicators of completeness, temporal correctness and stability of each stream with kinematic and dynamic residuals between the encoders, the drive currents, the inertial channel and the external estimate of the end-effector position. Threshold values are computed over a clean calibration interval using the median and the median absolute deviation; localisation is performed on a graph of inter-channel relations. The reproducible software experiment covers 864 scenarios, six types of distortion, four channels, three intensity levels and twelve repetitions. The proposed method reached a mean F1 of 0,862 with a 95% bootstrap interval [0,845; 0,879], whereas fixed rules and adaptive single-channel control yielded 0,622 and 0,791 respectively. The mean localisation accuracy was 0,843 and the false positive rate was 0,0059. The largest gain was obtained for constant offsets; for outliers and freezing, single-channel methods proved more competitive. The result is an interpretable module for input telemetry control suitable for embedding before data storage and analytical processing.

Combining Thinking Aloud and Eye Tracking

It is concluded that combining eye tracking and Thinking Aloud is feasible without compromising data quality and meaningful for comprehensive HCI evaluation.

Multi-Disease Prediction Using Machine Learning: A Web-Based Diagnostic Support System for Diabetes, Heart Disease, and Parkinson\'s Disease

A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.

HawkEye: Web Vulnerability Analysis and Security Audit Tool

HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.