Pembentukan Data Uji Menggunakan Algoritma Optimisasi Koloni Semut dan Pendekatan Teknik Pengujian Kotak Abu-Abu

Pengujian perangkat lunak dapat menjadi cara yang efektif untuk memperbaiki kualitas serta ketahanan perangkat lunak. Secara umum pengujian perangkat lunak dapat dilakukan dengan teknik kotak putih (white box) dan kotak hitam (black box). Teknik pengujian kotak abu-abu (gray box) dikenal sebagai teknik penguijan yang menggunakan pendekatan teknik pengujian kotak putih dan kotak hitam. Teknikpengujian kotak abu-abu memperluas kriteria cakupan logika dari teknik pengujian kotak putih dan menemukan semua kemungkinannya dari model desain perangkat lunak sepertipada teknik pengujian kotak hitam. Teknik pengujian kotak abu-abu menggunakan data seperti: UML Diagram, Model Arsitektur perangkat lunak, ataupun Finite State Machine Diagram (State Model) untuk membentuk kasus pengujian. Selain itu pengujian perangkat lunak dapat dilakukan dengan teknik Soft Computing dan Hard Computing. Teknik Hard Computing sulit diimplementasikan pada problematika yang ada saat ini. Sehinga teknik Soft Computing dapat menjadi alternatif yang digunakan dalam pengujian perangkat lunak. Teknik Soft Computing merupakan teknik yang lebih berfokus pada menginterpretasikan perilaku sistem dari pada hasil presisi. Teknik Soft Computing biasanya berdasarkan teknik logika fuzzy, jaringan saraf tiruan ataupun pengambilan keputusan berdasarkan nilai distribusi kemungkinan. Teknik pengujian perangkat lunak yang mengunakanteknil Soft Computing denganmetode pengambilan sampel berdasarkan nilai kemungkinan dikenal dengan teknik pengujian statistika. Berdasarkan penelitian terkait, algoritma Ant Colony Optimization (ACO) atau optimisasi koloni semut merupakan algoritma yang digunakan pada teknik pengujian statistika untuk membentuk data uji dengan kemampuan yang lebih baik dibandingkan algoritma lain seperti: Simulated Annealing (SA) serta algoritma genetika. Selain itu ACO juga memiliki hasil yang sebanding dengan algoritma Particle Swarm Optimization (PSO) atau optimisasi kawanan partikel. ACO diimplementasikan pada kode program perangkat lunak yang diuji untuk membentuk data uji berdasarkan nilai kemungkinan terbesar random data uji dari domain terpilih. Pemilihan data uji merupakan faktor utama yang menentukan keberhasilan dari suatu pengujian perangkat lunak. Sehingga pemilihan teknik yang tepat dapat membantu menunjang keberhasilan dalam pengujian perangkat lunak.Pada penelitian ini, ACO diimplementasikan berdasarkan teknik pengujian kotak abu-abu menggunakan diagram UML State Machine. Pembentukan data uji yang berkualitas adalah berdasarkan kecukupan kriteria percabangan yang dapat ditelusuri. Tujuan dari penelitian ini adalah untuk mendapatkan hasil perbandingan pembentukan data uji dengan teknik pengujian kotak abu-abu menggunakan diagram UML State Machine dan teknik pengujian struktur kotak putih menggunakan kode program. Hasil penelitian ini diharapkan mampu memberikan gambaran kualitas data uji yang dibentuk dari masing-masing teknik ========================================================================================================Software testing can be an effective way to improve software quality and reliability. In general, software testing techniques can be classified into White Box and Black Box. Gray Box testing technique is known as testing technique which is used both White Box and Black Box techniques. It extends the logical coverage criteria of white box method and finds all the possibility from the design model which is like black box method. Gray Box technique uses data such as: UML Diagram, Software Architecture Model, or Finite State Machine Diagram (State Model) to generate test cases. The other classification of software testing technique is Hard Computing technique and Soft Computing technique. Hard Computing technique is difficult to be implemented in today problems. And Soft Computing technique can be an alternative way which can be used in software testing. Soft Computing technique focuses on system behavior interpretation than precision result. Soft Computing technique is based on fuzzy logic, neural network, or decision making by probability distribution. Software testing which is used Soft Computing technique and its sampling method based on probability distribution, is known as statistical testing. Based on research, Ant Colony Optimization (ACO) Algorithm is algorithm which is used in statistical testing to generate test data, and its result better than another algorithm such as: Simulated Annealing (SA), and Genetic algorithm. Besides, its result is comparable with Paricle Swarm Optimization (PSO) algorithm. ACO was implemented in program code of software under test to generate test data based on the highest probability value of random test data from domain chosen. Test data selection is main factor which determines the software testing success. In this research, ACO was implemented based on Gray Box testing using UML State Machine Diagram. The quality of test data generated is based on branch coverage criteria. This research aims to get comparison result between Gray Box testing using UML State Machine Diagram and structural White Box Testing using program code in generating test data. The result of this research is expected to give description about the quality of test data generated from each technique

NASA Langley Scientific and Technical Information Output: 1996

This document is a compilation of the scientific and technical information that the Langley Research Center has produced during the calendar year 1996. Included are citations for Formal Reports, High-Numbered Conference Publications, High-Numbered Technical Memorandums, Contractor Reports, Journal Articles and Other Publications, Meeting Presentations, Technical Talks, Computer Programs, Tech Briefs, and Patents

Verifizierbare Entwicklung eines satellitenbasierten Zugsicherungssystems mit Petrinetzen

Nowadays model-based techniques are widely used in system design and development, especially for safety-critical systems such as train control systems. Given a design model, executable codes could be generated automatically from the model following certain transformation rules. A high-quality model of a system provides a good understanding, a favourable structure, a reasonable scale and abstraction level as well as realistic behaviours with respect to the concurrent operation of independent subsystems. Motivated by this principle, a first Coloured Petri Net (CPN) model of a satellite-based train control system (SatZB) with the capability of continuous simulation is developed employing the BASYSNET method which adopts Petri nets as the means of description during the whole development process. After establishing the system model, the verification tasks are identified based on the hazard analysis of the train control system. To verify the identified tasks for quality assurance, verification by means of simulation, formal analysis and testing is carried out considering the four representing system properties: function, state, structure and behaviour. For structural analysis, the concept of open nets is proposed to check the reproducibility of empty markings of scenario nets, the existence of dead transitions in the scenario nets, and the terminating states of the scenario nets. The system behaviour, in which states are involved, is investigated by reachability analysis. Unlike the conventional method of reachability analysis by calculating the state space of the Petri net, techniques based on Petri net unfoldings are introduced in this thesis. As to the functional verification, two model-based test generation techniques, i.e., CPN-based and SPENAT (Safe Place Transition Nets with Attributes)-based techniques, are presented. In this thesis, the proposed methods are exemplified by the application to the on-board module of SatZB model. According to the verification results, no errors were found in the module. Therefore, the confidence in the quality of the on-board module has been significantly increased.Heutzutage werden in zahlreichen Anwendungen modellbasierte Techniken zur Systementwicklung, insbesondere für sicherheitskritische Systeme wie Eisenbahnleit- und -sicherungssysteme, verwendet. Aus einem Design Modell kann dabei ausführbarer Code automatisch nach bestimmten Transformationsregeln generiert werden. Ein hochwertiges Modell des Systems bietet für die Entwicklung ein gutes Verständnis, eine günstige Struktur, eine angemessene Größenordnung und Abstraktionsebene als auch realistische Verhaltensweisen in Bezug auf den gleichzeitigen Betrieb von unabhängigen Subsystemen. Motiviert von dieses Prinzip wird ein erstes Farbige Petri-Netz (CPN)-Modell eines satellitenbasierten Zugsicherungssystem (SatZB) unter Verwendung der BASYSNET Methode entwickelt, der Petri-Netze als Beschreibungsmittel während des gesamten Entwicklungsprozesses nutzt. Dieses Modell bietet die Möglichkeit zur kontinuierlichen Simulation des Systemverhaltens. Nach der Erstellung des Systemmodells werden die Verifikationsaufgaben auf der Grundlage der Gefährdungsanalyse des Zugsicherungssystems identifiziert. Die abgeleiteten Bedingungen werden zur Qualitätssicherung durch Simulation, formale Analysen und Tests unter Berücksichtigung der vier Systemeigenschaften (Funktion, Zustand, Struktur und Verhalten) verifiziert. Für die Strukturanalyse wird das Konzept der offenen Netzen vorgeschlagen, um die Reproduzierbarkeit der leeren Markierungen der Szenario-Netze, die Existenz der Toten Transitionen in den Szenario-Netze, und die Abschluss Zustände der Szenario-Netze zu prüfen. Das Systemverhalten wird dabei durch Zustände beschrieben und durch eine Erreichbarkeitsanalyse untersucht. Im Gegensatz zu der konventionellen Methode, welche die Erreichbarkeit durch die Berechnung des Zustandsraums des Petri-Netzes analysiert, werden in dieser Arbeit Techniken auf Basis von Petri-Netz-Entfaltung eingeführt. Für die funktionale Verifikation werden zwei modellbasierte Testgenerierungstechniken, eine CPN-basierte und eine SPENAT (Sicheres Petrinetz mit Attributen)-basierte, vorgestellt. In dieser Arbeit werden die vorgeschlagenen Methoden durch die Anwendung auf das On-Board-Modul des SatZB-Modells veranschaulicht. Dabei wurden nach dem Abschluss der Prüfungen keine Fehler im Modul gefunden, wodurch das Vertrauen in die Qualität des On-Board-Moduls deutlich erhöht wurde

Avaliação da confiança no funcionamento de redes de campo : contribuição no domínio dos sistemas industriais de controlo

Tese de Doutoramento. Engenharia electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Discrete Deterministic and Stochastic Petri Nets

Petri nets augmented with timing specifications gained a wide acceptance in the area of performance and reliability evaluation of complex systems exhibiting concurrency, synchronization, and conflicts. The state space of time-extended Petri nets is mapped onto its basic underlying stochastic process, which can be shown to be Markovian under the assumption of exponentially distributed firing times. The integration of exponentially and non-exponentially distributed timing is still one of the major problems for the analysis and was first attacked for continuous-time Petri nets at the cost of structural or analytical restrictions. We propose a discrete deterministic and stochastic Petri net (DDSPN) formalism with no imposed structural or analytical restrictions where transitions can fire either in zero time or according to arbitrary firing times that can be represented as the time to absorption in a finite absorbing discrete-time Markov chain (DTMC). Exponentially distributed firing times are then approximated arbitrarily well by geometric distributions. Deterministic firing times are a special case of the geometric distribution. The underlying stochastic process of a DDSPN is then also a DTMC, from which the transient and stationary solution can be obtained by standard techniques. A comprehensive algorithm and some state space reduction techniques for the analysis of DDSPNs are presented, including the automatic detection of conflicts and confusions, which removes a major obstacle for the analysis of discrete-time models