Test case generation and fault diagnosis methods for communication protocols based on FSM and EFSM models

This thesis is concerned with some of the important open issues in test case generation from the FSM (finite state machine) and EFSM (extended finite state machine) models

Atomic Layer Deposition of Nanolaminate Structures of Alternating PbTe and PbSe Thermoelectric Films

For this study PbTe and PbSe thin film nanolaminates have been prepared on silicon substrates with native oxide by Atomic Layer Deposition (ALD) using lead(II)bis(2,2,6,6-tetramethyl-3,5-heptanedionato) (Pb(C11H19O2)(2), (trimethylsilyl) telluride ((Me3Si)2Te) and bis-(triethyl silyl) selane ((Et3Si)2Se) as ALD precursors for lead, tellurium and selenium. The experimental evidence revealed the ALD growth of lead telluride and lead selenide followed the Vollmer-Weber island growth mode. We found a strong dependence of the nucleation process on the temperature. In this paper, we present the optimized conditions for growing PbTe and PbSe thin film nanolaminates within the ALD process window range of 170 degrees C to 210 degrees C and discuss an early nano-scale PbTe/PbSe bilayer structure. Results of various physical characterizations techniques and analysis are reported

An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

This is the post-print version of the Article - Copyright @ 2011 ElsevierThe extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate = 100%). The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM

Estimating the feasibility of transition paths in extended finite state machines

There has been significant interest in automating testing on the basis of an extended finite state machine (EFSM) model of the required behaviour of the implementation under test (IUT). Many test criteria require that certain parts of the EFSM are executed. For example, we may want to execute every transition of the EFSM. In order to find a test suite (set of input sequences) that achieves this we might first derive a set of paths through the EFSM that satisfy the criterion using, for example, algorithms from graph theory. We then attempt to produce input sequences that trigger these paths. Unfortunately, however, the EFSM might have infeasible paths and the problem of determining whether a path is feasible is generally undecidable. This paper describes an approach in which a fitness function is used to estimate how easy it is to find an input sequence to trigger a given path through an EFSM. Such a fitness function could be used in a search-based approach in which we search for a path with good fitness that achieves a test objective, such as executing a particular transition, and then search for an input sequence that triggers the path. If this second search fails then we search for another path with good fitness and repeat the process. We give a computationally inexpensive approach (fitness function) that estimates the feasibility of a path. In order to evaluate this fitness function we compared the fitness of a path with the ease with which an input sequence can be produced using search to trigger the path and we used random sampling in order to estimate this. The empirical evidence suggests that a reasonably good correlation (0.72 and 0.62) exists between the fitness of a path, produced using the proposed fitness function, and an estimate of the ease with which we can randomly generate an input sequence to trigger the path

Using genetic algorithms to generate test sequences for complex timed systems

The generation of test data for state based specifications is a computationally expensive process. This problem is magnified if we consider that time con- straints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, sup- ported by tools, that addresses this issue by represent- ing the test data generation problem as an optimisa- tion problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be eas- ily adapted to be used with other evolutionary search techniques

Poly[μ3-β-alanine-aqua-μ4-sulfato-dilithium]

The title compound, [Li2(SO4)(C3H7NO2)(H2O)]n, is a coordination polymer in which the β-alanine residues remain in the zwitterionic form. The crystal structure consists of corrugated sheets of [LiO4] and [SO4] tetra­hedra parallel to (010) with the β-alanine mol­ecules located between the sheets. The two independent Li+ cations are four-coordinated by O atoms in a distorted tetra­hedral geometry. The crystal structure is formed by stacking of alternate organic and inorganic layers along the a axis. The crystal structure is further stabilized by N—H⋯O hydrogen bonds