1. What can we do better to make POPL/PLDI more relevant for the next generation? Ras Bodik (UC-Berkeley) Swarat Chaudhuri (Penn State) Sumit Gulwani (MSR Redmond) With inputs from:

2. How to make PL relevant 30 years from now? What problems will programming in 2040 be solving? Are we laying the foundations for 6 Turing awards from PL over the next 30 years? To think about these questions, let us consider: What are current technology trends? What are the unique strengths of PL as an area? 1 Questions

3. Computational devices getting cheaper –Thousands of super-computer programmers –Millions of traditional software developers –Billion end-users!! What is the programming model for these folks? Has mostly remained an HCI topic! Cloud Computing –Computing as a commodity –Has mostly remained architecture/systems topic!.... Are we keeping an eye on these trends? 2 Technology Trends

4. Intersects with most areas in computer science. Healthy mix of theory and practice.  Can be a breeding ground for inter-disciplinary disruptive innovation. What have we done to foster this? 3 Unique Strengths of PL

5. Human Computer Interaction –Visualization –Natural Language Processing Cognitive science, Education Systems biology, Social science –Computational Thinking in Sciences Gaming … 4 Interesting inter-disciplinary areas

6. Computational Thinking in Sciences Computational thinking is entering biology, social science, economics –example: agent-based generative social science Theory, algorithms, simulation already export C.S. ideas to sciences –Q: what will happen once the computational thinking takes foothold? –A: some notion of programming will follow Programming may be the tool for modeling and synthesis –example: understand and defend against biological attacks –growth of popularity: from the hands of researchers to practitioners –growth of scope: build large-scale models by composition Goal: develop languages for thinking and doing in sciences –Role of programming languages: bridge thinking and machines; make computational thinking accessible to masses; enable large models –State of the art: languages for sciences are “decades old”, did not receive the attention of mainstream programming languages: support for reuse and modularity, higher-level abstractions, static typing, program analysis, model checking.

7. Bring to attention technology trends. –Call for papers can include new futuristic topics. –Start accepting (short) papers that bring in new problem definitions or ad-hoc solutions. Encourage inter-disciplinary work. –Make it easy for outsiders to get in. –Have a separate category and/or use a different criterion (as for pearl papers). –Invited talks from experts in other communities. 6 Call for Action