project-codenet-20210511
- A decade ago, Marc Andreessen [famously wrote](https://a16z.com/2011/08/20/why-software-is-eating-the-world/) that "software is eating the world." Software now permeates every part of our existence; Google services combine for [2 billion lines of code](https://www.wired.com/2015/09/google-2-billion-lines-codeand-one-place/), and a modern vehicle [contains around](https://www.technologyreview.com/2012/12/03/181350/many-cars-have-a-hundred-million-lines-of-code/) 100 million lines of code. It's a monumental challenge to create, debug, maintain, and update these complex software systems. Recently, a fast-growing discipline known as ai for Code aims to help software developers improve their productivity by automating the software engineering process. AI for Code researchers have been leveraging technologies like NLP and augmenting them with code analysis and compilation techniques to perform a myriad of practical tasks, such as code search, summarization, and completion, as well as code-to-code translation. The discipline isn't limited to academic research either: Ruchir Puri, IBM Research's chief research scientist, discussed in a recent [podcast](https://open.spotify.com/episode/7gHPbVBHEgSdrACTow7Gql) how technologies from ai for Code are being used to modernize legacy software by helping migrate monolithic applications to microservices for IBM's enterprise clients.