This article presents resource-guided synthesis, a technique
for synthesizing recursive programs that satisfy both a functional specification and a symbolic resource bound. The technique is type-directed and rests upon a novel type system that
combines polymorphic refinement types with potential annotations of automatic amortized resource analysis. The type
system enables efficient constraint-based type checking and
can express precise refinement-based resource bounds. The
proof of type soundness shows that synthesized programs
are correct by construction. By tightly integrating program
exploration and type checking, the synthesizer can leverage
the user-provided resource bound to guide the search, eagerly rejecting incomplete programs that consume too many
resources. An implementation in the resource-guided synthesizer ReSyn is used to evaluate the technique on a range of recursive data structure manipulations. The experiments show
that ReSyn synthesizes programs that are asymptotically
more efficient than those generated by a resource-agnostic
synthesizer. Moreover, synthesis with ReSyn is faster than a
naive combination of synthesis and resource analysis. ReSyn
is also able to generate implementations that have a constant
resource consumption for fixed input sizes, which can be used
to mitigate side-channel attacks.
Mon 24 JunDisplayed time zone: Tijuana, Baja California change
14:00 - 15:30 | |||
14:00 20mTalk | Resource-Guided Program Synthesis PLDI Research Papers Tristan Knoth University of California at San Diego, USA, Di Wang Carnegie Mellon University, Nadia Polikarpova University of California, San Diego, Jan Hoffmann Carnegie Mellon University Media Attached | ||
14:20 20mTalk | Using Active Learning to Synthesize Models of Applications That Access Databases PLDI Research Papers Jiasi Shen Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology DOI Media Attached | ||
14:40 20mTalk | Synthesizing Database Programs for Schema Refactoring PLDI Research Papers Yuepeng Wang University of Texas at Austin, James Dong University of Texas at Austin, USA, Rushi Shah UT Austin, Işıl Dillig UT Austin Media Attached | ||
15:00 20mTalk | Synthesis and Machine Learning for Heterogeneous Extraction PLDI Research Papers Arun Iyer Microsoft Research, India, Manohar Jonnalagedda Inpher Inc., Switzerland, Suresh Parthasarathy Microsoft Research, India, Arjun Radhakrishna Microsoft, Sriram Rajamani Microsoft Research Media Attached |