Natural language research announcements

[Ming-Wei Chang <mchang21 AT uiuc.edu>]

Dear faculty and students,

A Ph.D. candidate of our department, Lin Tan, will give a talk (details
below) for the AIIS seminar at 4:00 pm, Mar 5th (this Thursday). The
room number is 3405. Hope to see you there!

/* Leveraging Code Comments to Improve Software Reliability */

Software reliability is critically important. This work focuses on
addressing fundamental challenges of software reliability: obtaining
accurate program specifications and discovering tools/languages
limitations. In this talk, I will show that comments provide a great
data source for obtaining important information, including
specifications and problems of current tools/languages. First, I will
present a novel approach, iComment, which is the first work to
automatically extract specifications from comments written in natural
language and use these specifications to detect comment-code
inconsistencies, i.e., software bugs and bad comments. Our evaluation
on large real-world software such as the Linux kernel, Mozilla, Apache
and Wine and 2 types of comments shows that iComment effectively
extracted 1832 specifications and detected 60 new bugs and bad
comments. iComment combines techniques from different areas, including
natural language processing (NLP), machine learning, information
retrieval, program analysis and statistics. To help explain the pros
and cons of extracting specifications from comments compared to
extracting specifications from code, I will briefly discuss AutoISES,
which infers security specifications by statically analyzing source
code, and then directly use these specifications to automatically
detect security bugs/violations. I will also briefly present,
cComment, which studies comment semantics and characteristics to
further understand what other comments can be utilized, how we can
utilize them, and what important problems/limitations they reveal. We
discovered many interesting findings that can guide the design of new
languages and tools for improving reliability, programmer
productivity, software evolution, etc.


Bio:

Lin Tan is a Ph.D. candidate in the Department of Computer Science at
the University of Illinois at Urbana-Champaign. Her research areas
include software systems, software reliability and security, with a
focus on using interdisciplinary techniques such as machine learning,
data mining, computer architecture and program analysis to address
systems reliability problems. She currently holds an IBM
Ph.D. Fellowship. Her recent work on architectural support for
intrusion detection has been successfully transferred and licensed
since 2006, and was selected into the IEEE Micro's Top Picks 2006.

Thank you,
Ming-Wei