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

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution activity. Currently, Computer-Aided Software Engineering (CASE), which is a set of software systems aimed to support set of software process activities, does this automation. While CASE tools prove its importance to develop high quality software, unfortunately CASE tools doesn’t cover all software development activities. This is because some activities need intellectual human skills, which are not currently available as computer software. To solve this shortcoming, Artificial Intelligence (AI) approaches are the ones that can be used to develop software tools imitating these intellectual skills. This paper presents the definition of Intelligent Computer Aided Software Engineering (I-CASE). The definition encompasses two steps. The first step is a clear decomposition of each basic software development activity to sub activities, and classify each one of them whether it is an intellectual or procedural job. The second step is the addressing of each intellectual (un-automated) one to proper AI-based approach. These tools may be integrated into a package as an Integrated Development Environment (IDE) or could be used individually. The discussion and the next implementation step are reported. Keywords: Software Engineering, CASE tools, Artificial Intelligenc

Autonomic Role and Mission Allocation Framework for Wireless Sensor Networks.

Pervasive applications incorporate physical components that are exposed to everyday use and a large number of conditions and external factors that can lead to faults and failures. It is also possible that application requirements change during deployment and the network needs to adapt to a new context. Consequently, pervasive systems must be capable to autonomically adapt to changing conditions without involving users becoming a transparent asset in the environment. In this paper, we present an autonomic mechanism for initial task assignment in sensor networks, an NP-hard problem. We also study on-line adaptation of the original deployment which considers real-time metrics for maximising utility and lifetime of applications and smooth service degradation in the face of component failures. © 2011 IEEE

Automated test sequence generation for finite state machines using genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Testing software implementations, formally specified using finite state automata (FSA) has been of interest. Such systems include communication protocols and control sections of safety critical systems. There is extensive literature regarding how to formally validate an FSM based specification, but testing that an implementation conforms to the specification is still an open problem. Two aspects of FSA based testing, both NP-hard problems, are discussed in this thesis and then combined. These are the generation of state verification sequences (UIOs) and the generation of sequences of conditional transitions that are easy to trigger. In order to facilitate test sequence generation a novel representation of the transition conditions and a number of fitness function algorithms are defined. An empirical study of the effectiveness on real FSA based systems and example FSAs provides some interesting positive results. The use of genetic algorithms (GAs) makes these problems scalable for large FSAs. The experiments used a software tool that was developed in Java

Improving fault coverage and minimising the cost of fault identification when testing from finite state machines

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Software needs to be adequately tested in order to increase the confidence that the system being developed is reliable. However, testing is a complicated and expensive process. Formal specification based models such as finite state machines have been widely used in system modelling and testing. In this PhD thesis, we primarily investigate fault detection and identification when testing from finite state machines. The research in this thesis is mainly comprised of three topics - construction of multiple Unique Input/Output (UIO) sequences using Metaheuristic Optimisation Techniques (MOTs), the improved fault coverage by using robust Unique Input/Output Circuit (UIOC) sequences, and fault diagnosis when testing from finite state machines. In the studies of the construction of UIOs, a model is proposed where a fitness function is defined to guide the search for input sequences that are potentially UIOs. In the studies of the improved fault coverage, a new type of UIOCs is defined. Based upon the Rural Chinese Postman Algorithm (RCPA), a new approach is proposed for the construction of more robust test sequences. In the studies of fault diagnosis, heuristics are defined that attempt to lead to failures being observed in some shorter test sequences, which helps to reduce the cost of fault isolation and identification. The proposed approaches and techniques were evaluated with regard to a set of case studies, which provides experimental evidence for their efficacy.Brunel Research Initiative and Enterprise Fund (BRIEF) Award from Brunel University and Departmental bursary from Department of Information Systems and Computing, Brunel Universit

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

A Review of System Development Systems

The requirements for a system development system are defined and used as guidelines to review six such systems: SAMM, SREM, SADT, ADS / SODA, PSL/PSA and Systematics. It is found that current system development systems emphasise only validation and user verification. They can perform relatively little on automatic file optimisation, process optimisation and maintenance.postprin