Abstract: With the development of big data, machine learning, and AI, existing software engineering techniques must be re-imagined to provide the productivity gains that developers desire. Furthermore, specialized hardware accelerators like GPUs or FPGAs have become a prominent part of the current computing landscape. However, developing heterogeneous applications is limited to a small subset of programmers with specialized hardware knowledge. To improve productivity and performance for data intensive and compute-intensive development, now is the time that the software engineering community should design new waves of refactoring, testing, and debugging tools for big data analytics and heterogeneous application development.
In this talk, we overview software development challenges in this new data-intensive scalable computing and heterogeneous computing domain. We describe examples of automated software engineering (debugging, testing, and refactoring) techniques that target this new domain and share lessons learned from building these techniques.
Bio: Miryung Kim is a Professor and Vice Chair of Graduate Studies in the Department of Computer Science at UCLA. Her current research focuses on software developer tools for data-intensive scalable computing and heterogeneous computing. Her group created automated testing and debugging for Apache Spark and conducted the largest scale study of data scientists in industry. Her group's Java bytecode debloating JDebloat made a tech transfer impact to Navy.
She produced 6 professors (Columbia, Purdue, two at Virginia Tech, etc). For her impact on nurturing the next generation of academics, she received the ACM SIGSOFT Influential Educator Award. She was a Program Co-Chair of FSE 2022. She was a Keynote Speaker at ASE 2019 and ISSTA 2022. She gave Distinguished Lectures at CMU, UIUC, UMN, UC Irvine, etc. She is a recipient of 10 Year Most Influential Paper Award from ICSME twice, an NSF CAREER award, a Microsoft Software Engineering Innovation Foundation Award, an IBM Jazz Innovation Award, a Google Faculty Research Award, an Okawa Foundation Research Award, and a Humboldt Fellowship from Alexander von Humboldt Foundation. She is an ACM Distinguished Member.
Abstract: With the development of big data, machine learning, and AI, existing software engineering techniques must be re-imagined to provide the productivity gains that developers desire. Furthermore, specialized hardware accelerators like GPUs or FPGAs have become a prominent part of the current computing landscape. However, developing heterogeneous applications is limited to a small subset of programmers with specialized hardware knowledge. To improve productivity and performance for data intensive and compute-intensive development, now is the time that the software engineering community should design new waves of refactoring, testing, and debugging tools for big data analytics and heterogeneous application development.
In this talk, we overview software development challenges in this new data-intensive scalable computing and heterogeneous computing domain. We describe examples of automated software engineering (debugging, testing, and refactoring) techniques that target this new domain and share lessons learned from building these techniques.
Bio: Miryung Kim is a Professor and Vice Chair of Graduate Studies in the Department of Computer Science at UCLA. Her current research focuses on software developer tools for data-intensive scalable computing and heterogeneous computing. Her group created automated testing and debugging for Apache Spark and conducted the largest scale study of data scientists in industry. Her group's Java bytecode debloating JDebloat made a tech transfer impact to Navy.
She produced 6 professors (Columbia, Purdue, two at Virginia Tech, etc). For her impact on nurturing the next generation of academics, she received the ACM SIGSOFT Influential Educator Award. She was a Program Co-Chair of FSE 2022. She was a Keynote Speaker at ASE 2019 and ISSTA 2022. She gave Distinguished Lectures at CMU, UIUC, UMN, UC Irvine, etc. She is a recipient of 10 Year Most Influential Paper Award from ICSME twice, an NSF CAREER award, a Microsoft Software Engineering Innovation Foundation Award, an IBM Jazz Innovation Award, a Google Faculty Research Award, an Okawa Foundation Research Award, and a Humboldt Fellowship from Alexander von Humboldt Foundation. She is an ACM Distinguished Member.