Multimodal Learning-Driven Software Analysis

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As software systems continue to take on ever more central roles in real--world production settings, their dependability has become increasingly critical. Software errors cost the global economy over $1 trillion per year and can cause devastating disruptions and human lives. Software developers spend around 50% of their work time finding and fixing software- related errors. This research program targets novel software analysis techniques for mitigating, detecting, and repairing errors automatically. We are at a pivotal point in which we can automatically learn patterns from massive amounts of digital data using deep learning. This enables us to tackle some of the recurring software engineering challenges, such as error detection and repair, by applying deep learning on data gathered from software developers in practice. Traditional automated error detection and program repair techniques rely on a set of predefined templates and rules that are limited to specific software error--types; adding support for a new type of error is manual, ad--hoc, and costly. Instead of hard--coding error detection and repair rules, we can automatically learn from the way developers make mistakes and repair those mistakes. The challenge here is how to best represent software systems for vectorization amenable to machine learning. Software analysis has traditionally revolved around the source code of the software. We propose to take a multimodal learning approach to error detection and repair in which in addition to the syntax and semantics of the software, its runtime behaviour is taken into account, through a combination of software analysis, computer vision, and machine learning algorithms. We will devise new techniques for textual and visual analysis of program execution traces and runtime visual artificants. This proposed program at the intersection of software analysis, deep learning, and computer vision will bolster our world- leading initiatives in automated error detection and repair and help further establish Canada as a leader in this important and timely research area. This line of work will have direct downstream benefits for businesses that adopt and apply machine learning-enabled software by enabling the detection and fixing of errors before deployment to real-world settings. We believe our multimodal learning approach will have a significant impact in devising more accurate software analysis techniques, not only for bug detection and repair, but for many related areas of software engineering where tasks can be automated by learning from examples.