Grant: Discovery Grants Program - Individual

Many organizations have increasingly adopted artificial intelligence (AI), particularly machine learning (ML), with the potential to deliver enormous value to industry and society. However, we experience failures and shortcomings in the resulting software systems. The main reason is the shift in the development paradigm generated by AI and ML. While ML tasks are typically related to the development and enhancement of ML algorithms and techniques, software engineering (SE) primarily focuses on the software requirements specification, testing, deployment, and evolution. ML applications differ from traditional software in that their logic is not explicitly programmed but instead automatically created by continuously learning from data. The ML-based systems' development process involves different activities, including data collection, training, and model evaluation. These tasks are mostly performed by ML and domain experts and, to a lesser extent, by software engineers. Nevertheless, due to the different approaches in the development of ML systems, the entire system's development requires new methods for already established SE processes or possibly wholly new approaches. In addition to the above, we are currently witnessing a new generation of software applications which involve high speed and high volume data streams, such as software applications related to the Internet of Things (IoT). IoT is about connecting any device to the Internet, enabling the digitization and service-based coordination of devices, vehicles, and other real-world elements. Therefore, it is no surprise that IoT has been recognized as a significant paradigm shift impacting both society and industry in numerous forms ranging from telemedicine to smart transportation, smart grids, and Industry 4.0. All these applications require efficient acquisition, processing, and management of high speed and high volume data before being used for training and reasoning by ML models. This research program aims to bring substantial advancements in modeling, designing, and deploying IoT data-driven machine learning applications. At the fundamental research level, the focus will be on establishing novel, and sound SE approaches for designing, evolving, and deploying practical ML-based systems. The research results will enable software engineers to transition ML-trained models to industry-strength production-quality ML applications. This research will benefit a diverse Canadian industry, such as smart factories, smart buildings, advanced manufacturing, in their automation processes by providing a structured approach to develop ML applications dealing with IoT data. The proposed research program will also stage a tremendous HQP training opportunity and equip Canadian industries with experts in engineering ML applications using IoT data, a profession believed to continue to be in high demand in the next five to ten years.