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ESEC/FSE 2021
Thu 19 - Sat 28 August 2021 Clowdr Platform
Wed 25 Aug 2021 17:25 - 17:30 - Analysis—Static Analysis and Symbolic Execution Chair(s): Vaibhav Sharma
Thu 26 Aug 2021 05:25 - 05:30 - Analysis—Static Analysis and Symbolic Execution Chair(s): Akond Rahman

TypeScript is a widely used optionally-typed language where developers can adopt “pay as you go” typing: they can add types as desired, and benefit from static typing. The “type annotation tax”; or manual effort required to annotate new or existing TypeScript can be reduced by a variety of automatic methods. Probabilistic machine-learning (ML) approaches work quite well. ML approaches use different inductive biases, ranging from simple token sequences to complex graphical neural network (GNN) models capturing syntax and semantic relations. More sophisticated inductive biases are hand-engineered to exploit the formal nature of software. Rather than deploying fancy inductive biases for code, can we just use “big data”; to learn natural patterns relevant to typing? We find evidence suggesting that this is the case. We present TypeBert, demonstrating that even with simple token-sequence inductive bias used in BERT-style models and enough data, type-annotation performance of the most sophisticated models can be surpassed.

Wed 25 Aug

Displayed time zone: Athens change

17:00 - 18:00
Analysis—Static Analysis and Symbolic ExecutionIdeas, Visions and Reflections / Research Papers / Demonstrations +12h
Chair(s): Vaibhav Sharma Amazon Web Services
17:00
10m
Paper
IDE Support for Cloud-Based Static Analyses
Research Papers
Linghui Luo Paderborn University, Germany, Martin Schäf Amazon Web Services, Daniel J Sanchez Amazon Alexa, Eric Bodden University of Paderborn; Fraunhofer IEM
DOI Pre-print
17:10
10m
Paper
A Bounded Symbolic-Size Model for Symbolic ExecutionArtifacts AvailableArtifacts Reusable
Research Papers
David Trabish Tel Aviv University, Shachar Itzhaky Technion, Noam Rinetzky Tel Aviv University
DOI Media Attached
17:20
5m
Paper
LLSC: A Parallel Symbolic Execution Compiler for LLVM IR
Demonstrations
Guannan Wei Purdue University, Shangyin Tan Purdue University, Oliver Bračevac Purdue University, Tiark Rompf Purdue University
DOI Pre-print
17:25
5m
Paper
Learning Type Annotation: Is Big Data Enough?
Ideas, Visions and Reflections
Kevin Jesse University of California at Davis, Prem Devanbu University of California at Davis, Toufique Ahmed University of California at Davis
DOI
17:30
30m
Live Q&A
Q&A (Analysis—Static Analysis and Symbolic Execution)
Research Papers

Thu 26 Aug

Displayed time zone: Athens change

05:00 - 06:00
Analysis—Static Analysis and Symbolic ExecutionIdeas, Visions and Reflections / Research Papers / Demonstrations
Chair(s): Akond Rahman Tennessee Tech University
05:00
10m
Paper
IDE Support for Cloud-Based Static Analyses
Research Papers
Linghui Luo Paderborn University, Germany, Martin Schäf Amazon Web Services, Daniel J Sanchez Amazon Alexa, Eric Bodden University of Paderborn; Fraunhofer IEM
DOI Pre-print
05:10
10m
Paper
A Bounded Symbolic-Size Model for Symbolic ExecutionArtifacts AvailableArtifacts Reusable
Research Papers
David Trabish Tel Aviv University, Shachar Itzhaky Technion, Noam Rinetzky Tel Aviv University
DOI Media Attached
05:20
5m
Paper
LLSC: A Parallel Symbolic Execution Compiler for LLVM IR
Demonstrations
Guannan Wei Purdue University, Shangyin Tan Purdue University, Oliver Bračevac Purdue University, Tiark Rompf Purdue University
DOI Pre-print
05:25
5m
Paper
Learning Type Annotation: Is Big Data Enough?
Ideas, Visions and Reflections
Kevin Jesse University of California at Davis, Prem Devanbu University of California at Davis, Toufique Ahmed University of California at Davis
DOI
05:30
30m
Live Q&A
Q&A (Analysis—Static Analysis and Symbolic Execution)
Research Papers