Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

This research extends the knowledge of live-virtual-constructive (LVC) and distributed virtual simulations (DVS) through a detailed analysis and characterization of their underlying computing architecture. LVCs are characterized as a set of asynchronous simulation applications each serving as both producers and consumers of shared state data. In terms of data aging characteristics, LVCs are found to be first-order linear systems. System performance is quantified via two opposing factors; the consistency of the distributed state space, and the response time or interaction quality of the autonomous simulation applications. A framework is developed that defines temporal data consistency requirements such that the objectives of the simulation are satisfied. Additionally, to develop simulations that reliably execute in real-time and accurately model hierarchical systems, two real-time design patterns are developed: a tailored version of the model-view-controller architecture pattern along with a companion Component pattern. Together they provide a basis for hierarchical simulation models, graphical displays, and network I/O in a real-time environment. For both LVCs and DVSs the relationship between consistency and interactivity is established by mapping threads created by a simulation application to factors that control both interactivity and shared state consistency throughout a distributed environment

Aspects of modelling and simulation of genetic algorithms : a formal approach

Genetic algorithms (GAs) are widely used in solving search and optimisation problems involving very large search spaces, or very many variables where closed form solutions are impractical due to the very size of the problems. This dissertation combines the two salient features of GAs, namely the temporal aspect of the evolutionary approach to solving problems at the heart of the GA, and the stochastic aspect of evolution arising from its reliance on basically random generation of new individuals with stringent selection in determining survival. The work centres around describing the formal modelling of GAs using a logical approach based on standard first-order logic combined with temporal logic and with probabilistic logic. These logics are combined into a unified logic, temporal-probabilistic logic (TPL) which is formulated in this work. The GA is then described using TPL as the main tool, and the working of the GA is detailed from its components to the actual processes by formulating a model of the GA. Several important parameters are described and analysed, as is the important mechanism of selection. A simple axiomatisation of the GA using TPL is described as well. Also presented are simulation of the workings of the genetic algorithm based on high-level Petri nets and experimentation with a genetic algorithm package providing experimental evidence centring on the various selection mechanisms for some of the theoretical results.reviewe

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method