Please note it's actually 4310 for the room, not the previous erroneously mentioned room number.
Abstracts for the talks:
NoFAQ: Synthesizing Command Repairs from Examples
Loris D'Antoni, Rishabh Singh, Michael Vaughn
Command-line tools are confusing and hard to use due to their cryp-
tic error messages and lack of documentation. Novice users often
resort to online help-forums for finding corrections to their buggy
commands, but have a hard time in searching precisely for posts
that are relevant to their problem and then applying the suggested
solutions to their buggy command. We present NoFAQ, a tool that
uses a set of rules to suggest possible fixes when users write buggy
commands that trigger commonly occurring errors. The rules are
expressed in a language called Fixit and each rule pattern-matches
against the user’s buggy command and corresponding error mes-
sage, and uses these inputs to produce a possible fixed command.
NoFAQ automatically learns Fixit rules from examples of buggy
and repaired commands. We evaluate NoFAQ on two fronts. First,
we use 92 benchmark problems drawn from an existing tool and
show that NoFAQ is able to synthesize rules for 81 benchmark
problems in real time using just 2 to 5 input-output examples for
each rule. Second, we run our learning algorithm on the examples
obtained through a crowd-sourcing interface and show that the
learning algorithm scales to large sets of examples.
The Care and Feeding of Wild-Caught Mutants
David Bingham Brown, Michael Vaughn, Ben Liblit, Thomas Reps
Mutation testing of a test suite and a program provides a way to
measure the quality of the test suite. In essence, mutation testing is
a form of sensitivity testing: by running mutated versions of the
program against the test suite, mutation testing measures the suite’s
sensitivity for detecting bugs that a programmer might introduce
into the program. This paper introduces a technique to improve
mutation testing that we call wild-caught mutants; it provides a
method for creating potential faults that are more closely coupled
with changes made by actual programmers. This technique allows
the mutation tester to have more certainty that the test suite is
sensitive to the kind of changes that have been observed to have
been made by programmers in real-world cases.
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