Active Learning for Software Rejuvenation

Speaker: Jiasi SHEN
         MIT

Title:   "Active Learning for Software Rejuvenation"

Date:    Wednesday, 23 February 2022

Time:    10:00am - 11:00am (HKT)

Zoom link:
https://hkust.zoom.us/j/928308079?pwd=MW9wTCtlSDd2MnViZGdNd2oreUpXZz09

Meeting ID:     928 308 079
Passcode:       20212022

Abstract:

Software now plays a central role in numerous aspects of human society.
Current software development practices involve significant developer
effort in all phases of the software life cycle, including the development
of new software, detection and elimination of defects and security
vulnerabilities in existing software, maintenance of legacy software, and
integration of existing software into more contexts, with the quality of
the resulting software still leaving much to be desired. The goal of my
research is to improve software quality and reduce costs by automating
tasks that currently require substantial manual engineering effort.

I present a novel approach for automatic software rejuvenation, which
takes an existing program, learns its core functionality as a black box,
builds a model that captures this functionality, and uses the model to
generate a new program. The new program delivers the same core
functionality but is potentially augmented or transformed to operate
successfully in different environments. This research enables the
rejuvenation and retargeting of existing software and provides a powerful
way for developers to express program functionality that adapts flexibly
to a variety of contexts. In this talk, I will show how we applied these
techniques to two classes of software systems, specifically
database-backed programs and stream-processing computations, and discuss
the broader implications of these approaches.


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Biography:

Jiasi Shen is a Ph.D. candidate at MIT EECS advised by Professor Martin
Rinard. She received her bachelor's degree from Peking University. Her
main research interests are in programming languages and software
engineering. She was named an EECS Rising Star in 2020.