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

USING VIRTUAL REALITY TO INVESTIGATE ‘PROTEAN’ ANTI-PREDATOR BEHAVIOUR

Prey animals have evolved a wide variety of behaviours to combat the threat of predation, many of which have received considerable empirical and theoretical attention and are generally well understood in terms of their function and mechanistic underpinning. However, one of the most commonly observed and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental investigation: so-called ‘protean’ behaviour. This is defined as ‘behaviour that is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey’. In this thesis, I have elucidated the mechanisms that allow protean behaviour to be an effective anti-predatory response. This was explored with two approaches. Firstly, through the novel and extremely timely use of virtual reality to allow human ‘predators’ to attack and chase virtual prey in three-dimensions from a first-person perspective, thereby bringing the realism that has been missing from previous studies on predator-prey dynamics. Secondly through the three-dimensional tracking of protean behaviour in a highly tractable model species, the painted lady butterfly (Vanessa cardui). I explored this phenomenon in multiple contexts. Firstly, I simulated individual protean prey and explored the effects of unpredictability in their movement rules with respect to targeting accuracy of human ‘predators’ in virtual reality. Next, I examined the concept of ‘protean insurance’ via digitised movements of the painted lady butterfly, exploring the qualities of this animals’ movement paths related to human targeting ability. I then explored how the dynamics of animal groupings affected protean movement. Specifically, I investigated how increasing movement path complexity interacted with the well-documented ‘confusion effect’. I explored this question using both an experimental study and a VR citizen science game disseminated to the general public via the video game digital distribution service ‘Steam’. Subsequently, I explored another phenomenon associated with groupings of prey items; the ‘oddity effect’, which describes the preferential targeting of phenotypically odd individuals by predators. Typically, this phenomenon is associated with oddity of colouration or size. In this case, I investigated whether oddity of protean movement patterns relative to other group members could induce a ‘behavioural oddity effect’. Finally, I used a specialised genetic algorithm (GA) that was driven by human performance with respect to targeting prey items. I investigated the emergent protean movement paths that resulted from sustained predation pressure from humans. Specifically, I examined the qualities of the most fit movement paths with respect to control evolutions that were not under the selection pressure of human performance (randomised evolution). In the course of this thesis, I have gained a deeper understanding of a near ubiquitous component of predator prey interactions that has until recently been the subject of little empirical study. These findings provide important insights into the understudied phenomenon of protean movement, which are directly applicable to predator –prey dynamics within a broad range of taxa

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite