Over a period of just about 5 years, the use of AI-based tools for software engineering has gone from being a very promising research investigation to indispensable features in modern developer environments. This talk will present AI-powered improvements and continuing transformation of Google’s internal software development. This viewpoint comes from extensive experience with developing and deploying AI-based tools to surfaces where Google engineers spend the majority of their time, including inner loop activities such as code authoring, review and search, as well as outer loop ones such as bug management and planning. Improvements in these surfaces are monitored carefully for productivity and developer satisfaction.

We will describe the challenges in how to align our internal efforts with the very fast moving field of LLMs. We need to constantly make judgment calls on technical feasibility, the possibility of iterative improvement and the measurability of impact as we decide what ideas to pursue for production level adaptation and adoption. The talk will go into several examples of this that we have gone through in recent past, and what we have learned in the process.

We will conclude the talk with changes that we expect to land in the next five years and some thoughts on how the community can collaborate better by focusing on good benchmarks.

Satish Chandra is a software engineer at Google, where he applies machine learning techniques to improve developer productivity and leads the work on internal developer infrastructure using these techniques.

Prior to Google, he has worked – in reverse chronological order – at Facebook, Samsung Research, IBM Research, and Bell Laboratories. His work has spanned many areas of programming languages and software engineering, including program analysis, type systems, software synthesis, bug finding and repair, software testing and test automation, and web technologies. His research has been widely published in leading conferences in his field, including POPL, PLDI, ICSE, FSE and OOPSLA. The projects he has led have had significant industrial impact: in addition to his work on ML-based developer productivity at Facebook, his work on bug finding tools shipped in IBM’s Java static analysis product, his work on test automation was adopted in IBM’s testing services offering, and his work at Samsung was included in Samsung’s Tizen IDE.

Satish Chandra obtained a PhD from the University of Wisconsin-Madison, and a B.Tech from the Indian Institute of Technology-Kanpur, both in computer science. He is an ACM Distinguished Scientist and an elected member of WG 2.4.