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Title: Developer-Centric Automated Debugging
Xiangyu Li
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Friday, December 6, 2019
Time: 3:00pm - 5:00pm (EST)
Location: Klaus 3100
Committee:
Alessandro Orso - Advisor, School of Computer Science, Georgia Institute of Technology
David Devecsery - School of Computer Science, Georgia Institute of Technology
Qirun Zhang - School of Computer Science, Georgia Institute of Technology
Spencer Rugaber - College of Computing, Georgia Institute of Technology
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 proposal, I present three projects, two completed and one on-going, that define
techniques to 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
slicing, which allows automated debugging techniques that rely on dynamic dependence
analysis to handle a broader range of faults. 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, supporting the developer-centric, automated debugging
workflow, which requires collecting, analyzing, and navigating the typically massive amount
of information in the failing execution, can be extremely expensive for non-trivial software
and failures. In fact, many existing interactive debugging techniques are shown to work
well on short executions, but fail to scale to even modest-length executions. My on-going
project aims to address this limitation. By utilizing a record-and-replay system, the technique
efficiently recreates the failing execution, breaks it down into smaller time slices, and
analyzes these slices in a parallelized, and on-demand fashion. I expect this approach to
scale to realistic, potentially long program executions while providing short response time
in the interactive debugging process.
