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ESEC/FSE 2021
Thu 19 - Sat 28 August 2021 Clowdr Platform
Wed 25 Aug 2021 17:00 - 17:10 - Analysis—Static Analysis and Symbolic Execution Chair(s): Vaibhav Sharma
Thu 26 Aug 2021 05:00 - 05:10 - Analysis—Static Analysis and Symbolic Execution Chair(s): Akond Rahman

Integrating static analyses into continuous integration (CI) or continuous delivery (CD) has become the best practice for assuring code quality and security.
Static Application Security Testing (SAST) tools fit well into CI/CD, because CI/CD allows time for deep static analyses on large code bases and prevents vulnerabilities in the early stages of the development lifecycle.
In CI/CD, the SAST tools usually run in the cloud and provide findings via a web interface.
Recent studies show that developers prefer seeing the findings of these tools directly in their IDEs.
Most tools with IDE integration run lightweight static analyses and can give feedback at coding time, but SAST tools used in CI/CD take longer to run and usually are not able to do so.
Can developers interact directly with a cloud-based SAST tool that is \textit{typically used in CI/CD} through their IDE?
We investigated if such a mechanism can integrate cloud-based SAST tools better into a developers' workflow than web-based solutions.
We interviewed developers to understand their expectations from an IDE solution.
Guided by these interviews, we implemented an IDE prototype for an existing cloud-based SAST tool. With a usability test using this prototype, we found that the IDE solution promoted more frequent tool interactions. In particular, developers performed code scans three times more often. This indicates better integration of the cloud-based SAST tool into developers' workflow. Furthermore, while our study did not show statistically significant improvement on developers' code-fixing performance, it did show a promising reduction in time for fixing vulnerable code.

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