Verifying web applications : from business level specifications to automated model-based testing

One of reasons preventing a wider uptake of model-based testing in the industry is the difficulty which is encountered by developers when trying to think in terms of properties rather than linear specifications. A disparity has traditionally been perceived between the language spoken by customers who specify the system and the language required to construct models of that system. The dynamic nature of the specifications for commercial systems further aggravates this problem in that models would need to be rechecked after every specification change. In this paper, we propose an approach for converting specifications written in the commonly-used quasi-natural language Gherkin into models for use with a model-based testing tool. We have instantiated this approach using QuickCheck and demonstrate its applicability via a case study on the eHealth system, the national health portal for Maltese residents.peer-reviewe

A model based design framework for safety verification of a semi-autonomous inspection drone

In this paper, we present a model based design approach to the development of a semi-autonomous control system for an inspection drone. The system is tasked with maintaining a set distance from the target being inspected and a constant relative pose, allowing the operator to manoeuvre the drone around the target with ease. It is essential that the robustness of the autonomous behaviour be thoroughly verified prior to actual implementation, as this will involve the flight of a large multi-rotor drone in close proximity to a solid structure. By utilising the Robotic Operating System to communicate between the autonomous controller and the drone, the same Simulink model can be used for numerical coverage testing, high fidelity simulation, offboard execution and final executable deploymen

PENERAPAN MODEL PEMBELAJARAN PROJECT BASED LEARNING DALAM MENINGKATKAN HASIL BELAJAR SISWA KELAS X PADA KONSEP PENANGANAN LIMBAH

Penelitian ini bertujuan untuk mengetahui keefektifan penerapan model pembelajaran Project Based Learning terhadap partisipasi dan hasil belajar siswa kelas X pada konsep penanganan limbah. Metode penelitian yang digunakan adalah quasi eksperimen dengan rancangan “one group pretest dan postest design”. Subjek penelitiannya adalah siswa kelas X MIA 5 tahun ajaran 2013-2014. Instrumen yang digunakan dalam penelitian berbentuk tes dan nontes, instrumen tes berupa soal objektif (pilihan ganda) 18 soal yang memenuhi kriteria hasil uji coba instrumen yaitu 16,6% mudah, 55,5% sedang dan 33,3% sukar. Instrumen tes diberikan pada saat pretest dan postest. Instrumen nontes berupa tabel skala sikap, lembar observasi psikomotor, lembar penilaian produk, angket respon siswa dan lembar observasi guru yang diberikan pada saat pembelajaran. Nilai rata-rata hasil pretest sebesar 50,98, rata-rata hasil postest 70,18 dengan peningkatan (N-gain) sebesar 0,41 dengan kriteria peningkatan sedang. Hasil penghitungan statistik untuk tes kognitif yang menggunakan uji t diperoleh thitung > ttabel yaitu 9,27 > 2,567 maka hipotesis signifikan dengan tingkat kepercayaan 99% (0,01). Nilai aktivitas belajar siswa rata-rata 79,9% memiliki kriteria baik, aspek psikomotor siswa rata-rata 88,6% memiliki kriteria sangat baik, dan penilaian produk yang dibuat oleh siswa rata-rata 74% dengan kriteria baik. Respon siswa selama proses pembelajaran memiliki rata-rata 3,93 dengan kriteria sangat baik (positif) terhadap pembelajaran, sedangkan aktivitas guru selama melaksanakan pembelajaran memiliki rata-rata 91,85% dengan kriteria sangat baik. Dari hasil penelitian ini dapat disimpulkan bahwa penerapan model pembelajaran Project Based Learning dapat meningkatkan partisipasi dan hasil belajar siswa pada konsep penanganan limbah dengan kriteria peningkatan sedang. Penulis menyarankan model pembelajaran Project Based Learning dapat digunakan pada materi yang lainnya, kemudia pada saat pembelajaran guru harus lebih menekankan materi pembelajaran yang bersangkutan kepada siswa, sehingga pemahaman siswa terhadap materi pembelajaran tertentu akan lebih meningkat dengan baik. Kata kunci: Project Based Learning, hasil belajar, konsep penanganan limbah

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018

CAD Model-based 3D Object Pose Estimation using an Edge-Based Nonlinear Model Fitting Algorithm

[[abstract]]This paper addresses the design of a model-based 3D object pose estimation algorithm, which is one of the major techniques to develop a robust robotic vision system using a monocular camera. The proposed system first extracts line features of a captured image by using edge detection and Hough transform techniques. Given a CAD model of the object-of-interest, the 6-DOF pose of the object can then be estimated via a novel edge-based nonlinear model fitting algorithm, which is a nonlinear optimization process for estimating the optimal object pose based on an edge-based distance metric. Experimental results validate the performance of the proposed system.[[notice]]補正完

Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft

Model based predictive control of a drum-type boiler

High fuel costs, stringent safety and pollution standards and the need to increase plant life-time have all driven the search for better boiler control. Traditional PID control cannot achieve the best possible results as it does not account for the strong interactions between the controlled variables. Much work has been done in the area of optimal control, but the improvements gained in performance have been lost to some extent by the difficulties involved in tuning such controllers. A linear predictive controller is presented in this paper, which is both fully multivariable and computationally efficient. It is also easy to tune as the controller tuning parameters are physically meaningful

Model predictive control based on LPV models with parameter-varying delays

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a Model Predictive Control (MPC) strategy based on Linear Parameter Varying (LPV) models with varying delays affecting states and inputs. The proposed control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. Moreover, the solution of the optimization problem associated with the MPC design is achieved by solving a series of Quadratic Programming (QP) problem at each time instant. This iterative approach reduces the computational burden compared to the solution of a non-linear optimization problem. A pasteurization plant system is used as a case study to demonstrate the effectiveness of the proposed approach.Peer ReviewedPostprint (author's final draft