Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing

Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based strategies have been confined to small interaction strengths (i.e., t≤3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. This thesis presents the design and implementation of a new interaction test generation strategy, known as the Particle Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test suites. Unlike other existing AI-based strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of PSTG is evaluated using several sets of benchmark experiments. Comparatively, PSTG consistently outperforms its AI counterparts and other existing strategies as far as the size of the test suite is concerned. Furthermore, the case study demonstrates the usefulness of PSTG for detecting faulty interactions of the input components

Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.Comment: 21 page

Validation of in situ applicable measuring techniques for analysis of the water adsorption by stone

As the water adsorbing behaviour (WAB) of stone is a key factor for most degradation processes, its analysis is a decisive aspect when monitoring deterioration and past conservation treatments, or when selecting a proper conservation treatment. In this study the performance of various non-destructive methods for measuring the WAB are compared, with the focus on the effect of the variable factors of the methods caused by their specific design. The methods under study are the contact-sponge method (CSM), the Karsten tube (KT) and the Mirowski pipe (MIR). Their performance is compared with the standardized capillary rise method (CR) and the results are analysed in relation to the open porosity of different lithotypes. Furthermore the effect of practical encumbrances which could limit the application of these methods was valuated. It was found that KT and CSM have complementary fields of investigation, where CSM is capable of measuring the initial water uptake of less porous materials with a high precision, while KT was found commodious for measuring longer contact times for more porous lithotypes. MIR showed too many discommodities, leading to unreliable results. To adequately compare the results of the different methods, the size of the contact area appears to be the most influential factor, whereas the contact material and pressure on the surface do not indicate a significant influence on the results. The study of these factors is currently being extended by visualization of the water adsorption process via X-ray and neutron radiography in combination with physico-mathematical models describing the WAB

Variable cavity volume tooling for high-performance resin infusion moulding

This article describes the research carried out by Warwick under the BAE Systems/EPSRC programme ‘Flapless Aerial Vehicles Integrated Interdisciplinary Research – FLAVIIR’. Warwick's aim in FLAVIIR was to develop low-cost innovative tooling technologies to enable the affordable manufacture of complex composite aerospace structures and to help realize the aim of the Grand Challenge of maintenance-free, low-cost unmanned aerial vehicle manufacture. This article focuses on the evaluation of a novel tooling process (variable cavity tooling) to enable the complete infusion of resin throughout non-crimp fabric within a mould cavity under low (0.1 MPa) injection pressure. The contribution of the primary processing parameters to the mechanical properties of a carbon composite component (bulk-head lug section), and the interactions between parameters, was determined. The initial mould gap (di) was identified as having the most significant effect on all measured mechanical properties, but complex interactions between di, n (number of fabric layers), and vc (mould closure rate) were observed. The process capability was low due to the manual processing, but was improved through process optimization, and delivered properties comparable to high-pressure resin transfer moulding

A Review of Accelerated Test Models

Engineers in the manufacturing industries have used accelerated test (AT) experiments for many decades. The purpose of AT experiments is to acquire reliability information quickly. Test units of a material, component, subsystem or entire systems are subjected to higher-than-usual levels of one or more accelerating variables such as temperature or stress. Then the AT results are used to predict life of the units at use conditions. The extrapolation is typically justified (correctly or incorrectly) on the basis of physically motivated models or a combination of empirical model fitting with a sufficient amount of previous experience in testing similar units. The need to extrapolate in both time and the accelerating variables generally necessitates the use of fully parametric models. Statisticians have made important contributions in the development of appropriate stochastic models for AT data [typically a distribution for the response and regression relationships between the parameters of this distribution and the accelerating variable(s)], statistical methods for AT planning (choice of accelerating variable levels and allocation of available test units to those levels) and methods of estimation of suitable reliability metrics. This paper provides a review of many of the AT models that have been used successfully in this area.Comment: Published at http://dx.doi.org/10.1214/088342306000000321 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Analyzing software data bindings in large-scale systems

One central feature of the structure of a software system is the coupling among its components (e.g., subsystems. modules) and the cohesion within them. The purpose of this study is to quantify ratios of coupling and cohesion and use them in the generation of hierarchical system descriptions. The ability of the hierarchical descriptions to localize errors by identifying error-prone system structure is evaluated using actual error data. Measures of data interaction, called data bindings, are used as the basis for calculating software coupling and cohesion. A 135,000 source line system from a production environment has been selected for empirical analysis. Software error data was collected from high-level system design through system test and from some field operation of the system. A set of five tools is applied to calculate the data bindings automatically, and cluster analysis is used to determine a hierarchical description of each of the system's 77 subsystems. An analysis of variance model is used to characterize subsystems and individual routines that had either many/few errors or high/low error correction effort

Visualization of the Significant Explicative Categories using Catanova Method and Non-Symmetrical Correspondence Analysis for Evaluation of Passenger Satisfaction

ANalysis Of VAriance (ANOVA) is a method to decompose the total variation of the observations into sum of variations due to different factors and the residual component. When the data are nominal, the usual approach of considering the total variation in response variable as measure of dispersion about the mean is not well defined. Light and Margolin (1971) proposed CATegorical ANalysis Of VAriance (CATANOVA), to analyze the categorical data. Onukogu (1985) extended the CATANOVA method to two-way classified nominal data. The components (sums of squares) are, however, not orthogonal. Singh (1996) developed a CATANOVA procedure that gives orthogonal sums of squares and defined test statistics and their asymptotic null distributions. In order to study which exploratory categories are influential factors for the response variable we propose to apply Non-Symmetrical Correspondence Analysis (D'Ambra and Lauro, 1989) on significant components. Finally, we illustrate the analysis numerically, with a practical example