James Birt

Optimizing testing efficiency with error-prone path identification and genetic algorithms

We present a method for optimizing software testing efficiency by identifying the most error prone path clusters in a program. We do this by developing variable length genetic algorithms that optimize and select the software path clusters which are weighted with sources of error indexes. Although various methods have been applied to detecting and reducing errors in a whole system, there is little research into partitioning a system into smaller error prone domains for testing. Exhaustive software testing is rarely possible because it becomes intractable for even medium sized software. Typically only parts of a program can be tested, but these parts are not necessarily the most error prone. Therefore, we are developing a more selective approach to testing by focusing on those parts that are most likely to contain faults, so that the most error prone paths can be tested first. By identifying the most error prone paths, the testing efficiency can be increased.

Predictive models for water sources with high susceptibility for bromine-containing disinfection by-product formation: implications for water treatment

This study examines a matrix of synthetic water samples designed to include conditions that favour brominated disinfection by-product (Br-DBP) formation, in order to provide predictive models suitable for high Br-DBP forming waters such as salinity-impacted waters. Br-DBPs are known to be more toxic than their chlorinated analogues, in general, and their formation may be favoured by routine water treatment practices such as coagulation/flocculation under specific conditions; therefore, circumstances surrounding their formation must be understood. The chosen factors were bromide concentration, mineral alkalinity, bromide to dissolved organic carbon (Br/DOC) ratio and Suwannee River natural organic matter concentration. The relationships between these parameters and DBP formation were evaluated by response surface modelling of data generated using a face-centred central composite experimental design. Predictive models for ten brominated and/or chlorinated DBPs are presented, as well as models for total trihalomethanes (tTHMs) and total dihaloacetonitriles (tDHANs), and bromide substitution factors for the THMs and DHANs classes. The relationships described revealed that increasing alkalinity and increasing Br/DOC ratio were associated with increasing bromination of THMs and DHANs, suggesting that DOC lowering treatment methods that do not also remove bromide such as enhanced coagulation may create optimal conditions for Br-DBP formation in waters in which bromide is present.

A Meta-theoretic Approach to Theory Integration in Information Systems

This research presents a meta-theoretic analysis of a nomological net for the purpose of identifying potential pathways for theory integration and multi-level theory development. Success in these two areas holds the potential to reduce theory clutter in IS and related social sciences. As a proof-of-concept, we identify theory domains that share ancillary variables or functional/structural components, using a 20-year sample of construct-based quantitative research published in core journals of the IS discipline. Identification of shared variables provides possible extension and integration development that will reduce theory fragmentation and may lead to discovery of fundamental unifying processes that underlie phenomena across disciplines.

Piloting mobile mixed reality simulation in paramedic distance education

New pedagogical methods delivered through mobile mixed reality (via a user-supplied mobile phone incorporating 3d printing and augmented reality) are becoming possible in distance education, shifting pedagogy from 2D images, words and videos to interactive simulations and immersive mobile skill training environments. This paper presents insights from the implementation and testing of a mobile mixed reality intervention in an Australian distance paramedic science classroom. The context of this mobile simulation study is skills acquisition in airways management focusing on direct laryngoscopy with foreign body removal. The intervention aims to assist distance education learners in practicing skills prior to attending mandatory residential schools and helps build a baseline equality between those students that study face to face and those at a distance. Outcomes from the pilot study showed improvements in several key performance indicators in the distance learners, but also demonstrated problems to overcome in the pedagogical method.

Pedagogy before Technology: A Design-Based Research Approach to Enhancing Skills Development in Paramedic Science Using Mixed Reality

In health sciences education, there is growing evidence that simulation improves learners’ safety, competence, and skills, especially when compared to traditional didactic methods or no simulation training. However, this approach to simulation becomes difficult when students are studying at a distance, leading to the need to develop simulations that suit this pedagogical problem and the logistics of this intervention method. This paper describes the use of a design-based research (DBR) methodology, combined with a new model for putting ‘pedagogy before technology’ when approaching these types of education problems, to develop a mixed reality education solution. This combined model is used to analyse a classroom learning problem in paramedic health sciences with respect to student evidence, assisting the educational designer to identify a solution, and subsequently develop a technology-based mixed reality simulation via a mobile phone application and three-dimensional (3D) printed tools to provide an analogue approximation for an on-campus simulation experience. The developed intervention was tested with students and refined through a repeat of the process, showing that a DBR process, supported by a model that puts ‘pedagogy before technology’, can produce over several iterations a much-improved simulation that results in a simulation that satisfies student pedagogical needs.

Identifying error proneness in path strata with genetic algorithms

In earlier work we have demonstrated that GA can successfully identify error prone paths that have been weighted according to our weighting scheme. In this paper we investigate whether the depth of strata in the software affects the performance of the GA. Our experiments show that the GA performance changes throughout the paths. It performs better in the upper, less in the middle and best in the lower layer of the paths. Although various methods have been applied for detecting and reducing errors in software, little research has been done into partitioning a system into smaller, error prone domains for software quality assurance. To identify error proneness in software paths is important because by identifying them, they can be given priority in code inspections or testing. Our experiments observe to what extent the GA identifies errors seeded into paths using several error seeding strategies. We have compared our GA performance with random path selection.