Mapping the Effectiveness of Automated Software Testing

Our society is in the middle of a vast technological transformation, with many labour-intensive tasks being automated with software. Software is permeating all aspects of our lives, with self- driving cars, online banking, AI assistants such as Siri and Alexa as notable examples that have direct implications into our safety, security and finances. Think for a moment what would happen if a software system that we highly trust fails to function as we expect? In October 2018, a Lion Air Boeing 737 MAX 8 airplane crashed killing everyone on board. Five months after this incident, the same model operated by Ethiopian Airlines crashed as well. Investigations into these two cases lead to the conclusion that the culprit was a bug, which was present in the software of Boeing 737 MAX 8. Such fatal cases arise because existing reliability models used for building safety critical systems cannot guarantee the absence of a bug, even with the very rigorous testing and verification that is done when building safety critical systems such as airplanes. This leads us to the following question, how trustworthy are current approaches for measuring the reliability of software systems, and how can we improve their ability to detect bugs?

Artificial Intelligence in carDiac arrEst (AIDE)

Out-of-Hospital Cardiac Arrest (OHCA) is a significant public health issue in Australia, with few survivors. This Australian-first study will examine whether an artificial intelligence (AI) decision support tool can assist Triple Zero call-takers to recognise OHCA, reduce the time-to-recognition, and increase the provision of life-saving interventions for OHCA.

Values-oriented Defect Fixing for Mobile Software Applications

This project aims to address critical problems with mobile applications that exhibit human values-based defects, by advancing our understanding, detection and fixing of such defects. Many mobile apps do not operate according to the essential values of their human users - e.g. inclusivity, accessibility, privacy, ethical behaviour, due care, emotions, etc - making them ineffective, underused, unfit for purpose or even dangerous. Expected outcomes include new theories, techniques and prototype tools for developers and end-users to detect and help fix values-based defects in mobile apps. Benefits include better, safer mobile apps for people and organisations and improved app developer productivity and competitiveness.

Investigation of the utility and the user experiences of widespread access to accurate emergency department wait times

Emergency Departments (EDs) undertake 1 in 10 medical consultations in Australia. Waits to see a doctor are highly variable and can cause significant frustration. Waits vary from one facility to another, from day to day and within a day with little or no visibility regarding wait time to consumers. Some Victorian EDs now use ED electronic information to predict wait times for patients. Transparent wait time information is likely to significantly improve patient experience of emergency care, improve ambulance workload distribution (by diverting appropriate patients to less busy facilities) offload time performance (and therefore response time) and emergency staff experience.

VALUE: improVing Decision-MAking ReLating To Software- Intensive ProdUcts And SErvices Development

This project will help companies to achieve operational excellence and transparency in their software development. These two objectives will be reached by empirical research that will help in gaining thorough understanding and knowledge on their software development processes. The project will run experiments to gather knowledge about the behavior of software technologies in different environments. Participating companies will learn more about the variables influencing their software development process so they will be able to control the identified variables to improve their process and make them more predictable. As a result of this project, decision makers in industry will no longer rely on intuition, but they will be supported by a body of empirical knowledge obtained from the experiments. This will allow companies to make better tradeoffs in decision making on software construction, since they will have an understanding of the benefits and risks of using certain software technologies in certain situations. This will lead to a more effective and efficient software development process and global competitive advantage.

EATDD: Empirical Assesment of Test-driven Development

This project will address these challenges through empirical studies (controlled experiments and case studies) with students and professionals using a variety of realistic tasks, and replicating the studies over the course of the project. The concrete objectives of this research project are: (i) to overcome the challenges, to shed light to unknowns and to uncover the hidden potential, associated with TDD; (ii) to achieve a deeper understanding of the dynamics of TDD in order to suggest necessary actions for improving the project planning practices, the productivity of the practitioners and the quality of the final software products; (iii) assessing the economical and business impact of TDD approach in a variety of tasks and contexts through a comprehensive empirical analysis of its effects on product quality and developer productivity as well as on secondary factors; (iv) to provide feedback and guidance to developers by measuring their adherence to known TDD patterns and to create empirically validated guidelines for easier and persistent adoption of TDD; (v) to generate a knowledge-base as a result of extensive empirical work throughout the project lifecycle. Foundational and empirical research methods will be utilized to collect and analyze data from academic and industrial environments during software development activities, in order to achieve these objectives. The results of the project are highly relevant for and applicable in practice.

PSDAP: Predicting Software Defects Across Project

This research aims at taking the current state of research and practice from retrospective analysis to prospective applications within the context of defect prediction by demonstrating the significant reductions in software testing related costs through the application of the developed methods and techniques. The goal is to extract useful knowledge from complex and heterogeneous data sources involved in producing software artifacts. Another goal is to create a public framework that is supported with data and toolsets for providing guidance to decision-making activities under uncertainty, targeted to testing researchers and professionals in software intensive industries.