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Title: Developer-Centric Automated Debugging
Xiangyu Li
Ph.D. Candidate in Computer Science
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Monday, April 26, 2021
Time: 9:00 AM - 11:00 AM (ET)
Location: https://bluejeans.com/648661869
Committee:
Dr. Alessandro Orso - Advisor, School of Computer Science, Georgia Institute of Technology
Dr. David Devecsery - School of Computer Science, Georgia Institute of Technology
Dr. Qirun Zhang - School of Computer Science, Georgia Institute of Technology
Dr. Spencer Rugaber - College of Computing, Georgia Institute of Technology
Dr. Marcelo d'Amorim - Department of Computer Science, Federal University of Pernambuco
Abstract:
Software debugging is an expensive activity that is responsible for a significant part of
software maintenance cost. In particular, locating faulty code (i.e., fault localization) is
one of the most challenging parts. In the past years, researchers have proposed many techniques
that aim at fully automating the task of fault localization. Although these techniques are
shown to be effective in reducing the amount of code developers need to inspect to locate
faults, there is growing evidence that they provide developers with limited help in realistic
debugging scenarios. I believe that a practical automated debugging technique should have
human developers at the center of the debugging process rather than trying to completely
replace them.
In this dissertation, I present three of my techniques that support developer-centric automated
debugging. First, I present ENLIGHTEN, an interactive, feedback-driven fault localization
technique. ENLIGHTEN supports and automates developers’ debugging workflow as follows.
It 1) uses traditional statistical fault localization (SFL) to formulate an initial hypothesis
of where the fault may be; 2) identifies a relevant subset of execution that can help support
or refute the formulated hypothesis; 3) presents the developer with a query about the identified
execution subset in the form of a correctness question about the input-output relation of the
partial execution; 4) refines its hypothesis of the fault by using the developer’s feedback; and
5) repeats these steps until the fault is found. Second, I discuss my work on improving the
accuracy of dynamic dependence analysis, which is a powerful tool for developers to investigate
program behavior in an interactive debugging setting and a foundation for many automated debugging
techniques to model dynamic execution semantics. I present my finding that existing dynamic
dependence analysis techniques could miss the cause-effect relations between faults and the
observed failures if the faulty program states propagate via incorrect computation of memory
addresses. To address this limitation, I define the concept of potential memory-address dependence,
which explicitly represents this type of causal relations, and describe an algorithm that computes
it. Third, I present TESSERACT, a technique that improves the scalability of dynamic dependency
analysis in the context of interactive debugging. Many existing dependency-based debugging
techniques are shown to work well on short executions, but fail to scale to even modest-length ones.
TESSERACT addresses this limitation by utilizing a record-and-replay system to efficiently recreate
the failing execution, break it down into small time slices, and analyze these slices in a
parallelized, and on-demand fashion.
