Modern code review (MCR) is a widely adopted software quality assurance practice in the contemporary software industry. As software developers spend significant amounts of time on MCR activities, even a small improvement in MCR effectiveness will incur significant savings. As most of the MCR activities are heavily dependent on manual work, there are significant opportunities to improve effectiveness through tool support. To address the challenges, the primary objective of my proposed dissertation is to improve the effectiveness of modern code reviews with the automation of reviewer selection and bug identification. On this goal, I propose three studies. The first study aims to investigate the notion of useful MCRs and factors influencing MCR usefulness. The second study aims to develop a reviewer recommendation system that leverages a reviewer’s prior history of providing useful feedback under similar contexts. Finally, the third study aims to improve the effectiveness of static analysis tools by leveraging bugs identified during prior reviews.