Reinventing Testing for Big Data and Heterogeneous Computing-Miryung Kim (UCLA)

Miryung Kim (UCLA) visits KTH Sweden to give a talk on:

### Reinventing Testing for Big Data and Heterogeneous Computing

The rise of big data, machine learning, and AI necessitates re-evaluating automated software testing techniques to achieve desired developer productivity gains. In this talk, I will reflect on my group’s experience of designing custom fuzzers for data-intensive computing and heterogeneous hardware domains. I will discuss the need to encode domain-specific constraints, custom feedback guidance, custom search strategies, and custom mutation operators to make the fuzzing solutions effective for a specialized domain. Then, reflecting on this manual specialization effort, I will discuss a new direction on how we should strive to bootstrap a domain-specific testing engine with minimal manual effort. Toward this vision of bootstrapping a domain-specific testing engine without paying too much, I will share several ongoing effort to find the right balance between the universality of a fuzzer and its effectiveness in a specialized domain: (1) custom mutation synthesis from examples, (2) automated grammar refinement to constrain fuzzing, (3) LLM-guided constraint-generation for mutation, and (4) a lightweight DSL for context-guided input generation.

About Miryung Kim:
Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA’s Computer Science Department. A pioneer in big data software engineering, she led research on the role of data scientists in the software industry and redesigned developer tools for big data analytics including Apache Spark. Her work helped formalize the “data scientist” role at Microsoft, which in turn spurred a proliferation of new data science and AI programs in universities. Code clones cause redundant developer effort across organizations. Prof Kim was among the first to analyze recurring software changes using large-scale data from GitHub and Stack Overflow. Her work
advanced understanding of API stability, refactoring identification, and large-scale refactoring in industry. Professor Kim has mentored several PhD students and postdoctoral researchers, eight of whom now hold faculty appointments at institutions such as Columbia and Purdue. She has received the ACM SIGSOFT Influential Educator Award, served as FSE Program Co-Chair,
delivered keynotes at ASE and ISSTA, and given distinguished lectures at CMU and UIUC. She has also worked with Microsoft Research and is an Amazon Scholar at AWS.

Room 4523. See https://www.kth.se/places/room/id/a1b984c5-e064-403a-a7be-7f1aa4fa9690?l=en