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PLDI 2021
Sun 20 - Sat 26 June 2021 PLDI

The problem of synthesizing specifications of undefined procedures has a broad range of applications, but the usefulness of the generated specifications depends on their quality. In this paper, we propose a technique for finding maximal and non-vacuous specifications. Maximality allows for more choices for implementations of undefined procedures, and non-vacuity ensures that safety assertions are reachable.
To handle programs with complex control flow, our technique discovers not only specifications but also inductive invariants. Our iterative algorithm lazily generalizes non-vacuous specifications in a counterexample-guided loop. The key component of our technique is an effective non-vacuous specification synthesis algorithm. We have implemented the approach in a tool called HornSpec, taking as input systems of constrained Horn clauses. We have experimentally demonstrated the tool's effectiveness, efficiency, and the quality of generated specifications on a range of benchmarks.

Fri 25 Jun

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09:00 - 09:40
Talks 5A: Machine Learning and Probabilistic ProgrammingPLDI at PLDI-A +12h
09:00
5m
Talk
DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads
PLDI
Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University
DOI
09:05
5m
Talk
Provable Repair of Deep Neural Networks
PLDI
Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis
DOI Pre-print Media Attached
09:10
5m
Talk
DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning
PLDI
Kevin Ellis Cornell University, Catherine Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology
DOI
09:15
5m
Talk
Specification Synthesis with Constrained Horn Clauses
PLDI
Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore
DOI
09:20
5m
Talk
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
PLDI
Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research
DOI
09:25
5m
Talk
Sound Probabilistic Inference via Guide Types
PLDI
Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin
DOI
09:30
5m
Talk
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
PLDI
Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology
DOI
09:35
5m
Talk
Quantitative Analysis of Assertion Violations in Probabilistic Programs
PLDI
Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology
DOI
21:00 - 21:40
Talks 5A: Machine Learning and Probabilistic ProgrammingPLDI at PLDI-A
21:00
5m
Talk
DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads
PLDI
Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University
DOI
21:05
5m
Talk
Provable Repair of Deep Neural Networks
PLDI
Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis
DOI Pre-print Media Attached
21:10
5m
Talk
DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning
PLDI
Kevin Ellis Cornell University, Catherine Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology
DOI
21:15
5m
Talk
Specification Synthesis with Constrained Horn Clauses
PLDI
Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore
DOI
21:20
5m
Talk
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
PLDI
Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research
DOI
21:25
5m
Talk
Sound Probabilistic Inference via Guide Types
PLDI
Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin
DOI
21:30
5m
Talk
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
PLDI
Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology
DOI
21:35
5m
Talk
Quantitative Analysis of Assertion Violations in Probabilistic Programs
PLDI
Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology
DOI