An efficient programming-by-example framework

Due to the ubiquity of computing, programming has started to become an essential skill for an increasing number of people, including data scientists, financial analysts, and spreadsheet users. While it is well known that building any complex and reliable software is difficult, writing even simple scripts is challenging for novices with no formal programming background. Therefore, there is an increasing need for technology that can provide basic programming support to non-expert computer end-users. Program synthesis, as a technique for generating programs from high-level specifications such as input-output examples, has been used to automate many real-world programming tasks in a number of application domains such as spreadsheet programming and data science. However, developing specialized synthesizers for these application domains is notoriously hard. This dissertation aims to make the development of program synthesizers easier so that we can expand the applicability of program synthesis to more application domains. In particular, this dissertation describes a programming-by-example framework that is both generic and efficient. This framework can be applied broadly to automating tasks across different application domains. It is also efficient and achieves orders of magnitude improvement in terms of the synthesis speed compared to existing state-of-the-art techniques.