Automated Testing of Data Survivability and Restorability

Regular data backups are fundamental for protection against cyber-attacks and damage to infrastructure. To ensure a successful restoration, backed up data must be tested regularly for restorability to the company’s current environment. Cloud providers generally test their backedup data, but a testing framework is also required for locally stored files and databases. The paper proposes an automated test framework that validates the continued usability of backed up data for target restoration environments. The framework tests backups of Excel files, MySQL and Postgres databases, PDF documents and flat files.

Monitoring Riders Pattern For Automatic License Issuing System

This paper presents about the mechanization of driving permit test framework and informing the aftereffect of the applicant remotely. Normally, in driving test an up-and comer applied for license need to drive over a shut hover path before the trained professionals. The up-and-comer needs to roll over the way with explicit guidelines and on the off chance that he neglects to do so he will be excluded and should attempt next time. These specialists watch the mistakes of the up-and-comers physically. In this paper, the Arduino framework with important sensor modules has been produced for viewing the contender for getting their permit. By utilizing this framework, the applicant who take up the test are checked and the outcome climate the competitor is passed or fizzled is refreshed to the up-and-comer just as the specialists remotely utilizing IOT and GSM modules. This checking of the driving test ground is done autonomously using the Arduino structure. This framework is produced for improving the norms of permit giving instrument to improve street wellbeing

Robustness Testing Framework For Neural Network Image Classifier

Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interference intensity, a variety of robustness test sets are constructed, and a robustness testing framework suitable for neural network classifiers is proposed. And the feasibility and effectiveness of the test framework and method are verified by testing LENET-5 and the model reinforced by the adversavial training.

Development of Chemistry Tests for Students’ on Reaction Rate Subject Matter Based on Critical Thinking Skills using Framework DOT Test

This study aims to develop a valid and reliable test to measure the critical thinking skills of the high school students of class XI on reaction rate subject matter using the Danczak – Overton – Thompson test framework (DOT test). The research method used in this research is development and validation. In this article, there are three stages of test development, namely the analysis of KI and KD in Chemistry of 2013 curriculum, analysis of the DOT test framework, and the preparation of the test predictions. The development stages produce a draft instrument that is ready to be validated. The developed instrument consists of 30 test questions. The validation stage includes the content validity test by 5 expert judgments. In the validity test, the calculated CVI value is 0.97 so that the instrument is valid. Based on the analysis of research data, it is concluded that the chemistry test based on critical thinking skills that was developed had a good validity value to measure students' critical thinking skills on the reaction rate subject matter.

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

After decades of advances in techniques of automatic parallelization, software developers can count, today, on many different tools that transform a program, so that it runs in parallel. Yet, most of these tools are still considered research artifacts. They contain latent bugs, often consequence of a sparsity of testing frameworks for autoparallelizers. This dissertation describes one such framework: AutoParBench - the product of a cooperation between UFMG's Compilers Lab, and Lawrence Livermore National Laboratory. AutoParBench is today publicly available, and has been successfully used to find three zero-day bugs in the Intel C Compiler. Its usage also uncovered problems in more research-oriented tools: 2 bugs in DawnCC, 4 in Rose AutoPar and 2 in Cetus. All these bugs have been confirmed, and some of them have been already fixed as an aftermath of this work.