Eat and Dream: effective automatic testing and debugging for real-life embedded wireless communications software. Embedded software is a key enabling technology for the majority of Australian manufacturing industries, including strategically important sectors such as the automotive industry. Embedded wireless communication technologies are playing an increasingly significant role in Australia with a wide range of critical applications ranging from natural disaster early warning to personal healt ....Eat and Dream: effective automatic testing and debugging for real-life embedded wireless communications software. Embedded software is a key enabling technology for the majority of Australian manufacturing industries, including strategically important sectors such as the automotive industry. Embedded wireless communication technologies are playing an increasingly significant role in Australia with a wide range of critical applications ranging from natural disaster early warning to personal health monitoring. Embedded wireless communications software, however, is difficult to test and debug owing to the complexity of the operational environment and complications arising from the interplay between software and hardware. This project will develop an effective and automatic technology to alleviate these difficulties and achieve higher quality software.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100035
Funder
Australian Research Council
Funding Amount
$3,009,457.00
Summary
Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their s ....Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their support during software engineering. The intended outcomes are enhanced theory, models, tools and capability for next-generation software engineering with these critical elements. Significant benefits are expected to include greatly improved software quality, developer productivity and cost savings.Read moreRead less
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. Exp ....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.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101057
Funder
Australian Research Council
Funding Amount
$424,140.00
Summary
Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the softwar ....Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the software industry deliver to users high quality software with improved reliability and safety, and increase education quality for students learning to code via automated feedback generation.Read moreRead less
A data driven paradigm for service-oriented system engineering. This project aims to design and develop a data driven paradigm for service-oriented system engineering that allows system engineers and domain experts in different domains to build software systems easily in order to enable fast technology transfer within and across domain boundaries. This model integrates and automates a suite of efficient approaches for system structure determination, validation and recommendation based on keyword ....A data driven paradigm for service-oriented system engineering. This project aims to design and develop a data driven paradigm for service-oriented system engineering that allows system engineers and domain experts in different domains to build software systems easily in order to enable fast technology transfer within and across domain boundaries. This model integrates and automates a suite of efficient approaches for system structure determination, validation and recommendation based on keyword search, subgraph isomorphism and substructure query techniques. This project is expected to significantly accelerate the application of new technologies, for example, big data analytics and Internet of Things, in many of Australia's critical domains such as e-Health, smart cities, and cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101081
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t ....Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.Read moreRead less
Intelligent Incident Management for Software-Intensive Systems. This project aims to develop intelligent incident management methods for software-intensive systems. Incidents are unplanned system interruptions or outages that could affect the normal operations of an organization and cause huge economic loss. This project expects to develop innovative, Artificial Intelligence (AI) based methods for automated incident management, including incident detection, incident identification, and incident ....Intelligent Incident Management for Software-Intensive Systems. This project aims to develop intelligent incident management methods for software-intensive systems. Incidents are unplanned system interruptions or outages that could affect the normal operations of an organization and cause huge economic loss. This project expects to develop innovative, Artificial Intelligence (AI) based methods for automated incident management, including incident detection, incident identification, and incident triage. Expected outcomes of the project include a set of novel methods and tools that can facilitate incident diagnosis and resolution. This project will provide significant benefits, such as improving the availability of software-intensive systems and reducing the economic loss caused by the incidents. Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Automating data placement and movement for explicitly managed memory hierarchies. Efficient management of explicitly managed memory hierarchies is essential, making a difference often by one order of magnitude in performance. Compiler-directed techniques promise to take the burden of memory management from the programmer and enable significant performance potential for a broader community, resulting in higher productivity.
Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project ....Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project are a family of new context-aware techniques to verify and validate complex behaviours in autonomous driving. This should provide significant benefits, such as safe autonomous driving systems and the improved journey experience and security for road users.Read moreRead less