SAS 2019, EXTENDED Deadline: April 22 Abstract, April 25 Paper and Artifact: 26th Static Analysis Symposium, CfP

by Bor-Yuh Evan Chang, April 11, 2019

SAS 2019: EXTENDED Deadline: April 22 Abstract, April 25 Paper and Artifact. Submit at with a special call for Trends in Static Analysis on static analysis and machine learning.


                               SAS 2019

                    26th Static Analysis Symposium

           Part of the 3rd World Congress on Formal Methods

                  Porto, Portugal, October 8-11, 2019




- New Abstract Submission: Monday, April 22, 2019 (firm)

- Extended Paper Submission: Thursday, April 25, 2019 (firm)

- Artifact Submission: Thursday, April 25, 2019 (firm)

- Author Response: Friday-Monday, May 31-June 3, 2019

- Notification: Friday, June 14, 2019

- Conference: Wednesday-Friday, October 9-11, 2019

All deadline times are AoE.


Static analysis is widely recognized as a fundamental tool for program verification, bug detection, compiler optimization, program understanding, and software maintenance. The series of Static Analysis Symposia has served as the primary venue for the presentation of theoretical, practical, and application advances in the area. The 26th Static Analysis Symposium, SAS 2019, will be held in Porto, Portugal. Previous symposia were held in Freiburg, New York, Edinburgh, Saint-Malo, Munich, Seattle, Deauville, Venice, Perpignan, Los Angeles, Valencia, Kongens Lyngby, Seoul, London, Verona, San Diego, Madrid, Paris, Santa Barbara, Pisa, Aachen, Glasgow, and Namur.


The technical program for SAS 2019 will consist of invited lectures and presentations of refereed papers. Contributions are welcomed on all aspects of static analysis, including, but not limited to:

- Abstract domains

- Abstract interpretation

- Automated deduction

- Data flow analysis

- Debugging

- Deductive methods

- Emerging applications

- Model checking

- Program optimizations and transformations

- Program synthesis

- Program verification

- Security analysis

- Tool environments and architectures

- Theoretical frameworks

- Type checking


New in 2019, special sessions will be organized around a trending topic in static analysis. For SAS 2019, we especially solicit Trends in Static Analysis contributions around the emerging convergence of static analysis and machine learning. Trends contributions are welcome on this convergence broadly construed, including, but not limited to:

- Scaling static analysis to "big code"

- Data-driven static analysis

- Assuring machine learning with static analysis

Trends contributions will be refereed in the same manner and with the same standards as other contributions.


Submissions can address any programming paradigm, including concurrent, constraint, functional, imperative, logic, object-oriented, aspect, multi-core, distributed, and GPU programming.

- Papers must describe original work, be written and presented in English, and must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with refereed proceedings.

- Submitted papers will be judged on the basis of significance, relevance, correctness, originality, and clarity.

- They should clearly identify what has been accomplished and why it is significant.

- Paper submissions should not exceed 18 pages in Springer’s Lecture Notes in Computer Science (LNCS) format, excluding bibliography and well-marked appendices. Program Committee members are not required to read the appendices, and thus papers must be intelligible without them.


As in previous years, we encourage authors to submit a virtual machine image containing any artifacts and evaluations presented in the paper. The goal of the artifact submissions is to strengthen our field’s scientific approach to evaluations and reproducibility of results. The virtual machines will be archived on a permanent Static Analysis Symposium website to provide a record of past experiments and tools, allowing future research to better evaluate and contrast existing work.

Artifact submission is optional. We accept only virtual machine images that can be processed with VirtualBox. The artifact should come with a virtual machine (VM) image and step-by-step instructions:

- Virtual machine image: The VM image must be bootable and contain all the necessary libraries installed. Please ensure that the VM image can be processed with VirtualBox. When preparing your artifact, please make it light as possible.

- Step-by-step instructions: It should clearly explain how to reproduce the results that support your paper’s conclusions. We encourage the authors to have easy-to-run scripts. Also, you should explain how to interpret the output of the artifact. Please provide an estimated execution time for each instruction.

Please follow the instructions below to submit your artifact:

- Make the VM image and the instruction document into single compressed archive file using zip or gzip. Use your paper number for the name of the archive file.

- Upload the archive file to well-known storage service such as Dropbox or Google Drive and get the sharable link of it.

- Run a checksum function with the archive file and make a text file that contains the link to the archive file and the checksum the result. Submit the text file via the submission page. The submission form has a place for the artifact submission.


SAS 2019 will use a lightweight double-blind reviewing process. Following this process means that reviewers will not see the authors’ names or affiliations as they initially review a paper. The authors’ names will then be revealed to the reviewers only once their reviews have been submitted.

To facilitate this process, submitted papers must adhere to the following:

- Author names and institutions must be omitted and

- References to the authors’ own related work should be in the third person (e.g., not “We build on our previous work …” but rather “We build on the work of …”). The purpose of this process is to help the reviewers come to an initial judgment about the paper without bias, not to make it impossible for them to discover the authors if they were to try. Nothing should be done in the name of anonymity that weakens the submission, makes the job of reviewing the paper more difficult, or interferes with the process of disseminating new ideas. For example, important background references should not be omitted or anonymized, even if they are written by the same authors and share common ideas, techniques, or infrastructure. Authors should feel free to disseminate their ideas or draft versions of their paper as they normally would. For instance, authors may post drafts of their papers on the web or give talks on their research ideas.


During the author response period, authors will be able to read reviews and respond to them as appropriate.


Since 2014, the program committee of each SAS conference selects a paper for the Radhia Cousot Young Researcher Best Paper Award, in memory of Radhia Cousot, and her fundamental contributions to static analysis, as well as being one of the main promoters and organizers of the SAS series of conferences.


- Bor-Yuh Evan Chang (University of Colorado Boulder)


- Josh Berdine (Facebook)

- Marc Brockschmidt (Microsoft Research)

- Yu-Fang Chen (Academia Sinica)

- Roberto Giacobazzi (Università di Verona)

- Ben Hardekopf (University of California, Santa Barbara)

- Thomas Jensen (INRIA)

- Ranjit Jhala (University of California, San Diego)

- Andy King (University of Kent)

- Shuvendu Lahiri (Microsoft Research)

- Akash Lal (Microsoft Research, India)

- Francesco Logozzo (Facebook)

- Jan Midtgaard (University of Southern Denmark)

- Antoine Miné (Sorbonne Université)

- Anders Møller (Aarhus University)

- David Monniaux (VERIMAG/CNRS/Université Grenoble Alpes)

- Kedar Namjoshi (Bell Labs, Nokia)

- Sylvie Putot (LIX, École polytechnique)

- Veselin Raychev (DeepCode AG)

- Xavier Rival (INRIA/CNRS/ENS/PSL*)

- Sriram Sankaranarayanan (University of Colorado Boulder)

- Tachio Terauchi (Waseda University)

- Aditya V. Thakur (University of California, Davis)

- Tomas Vojnar (FIT, Brno University of Technology)

- Kwangkeun Yi (Seoul National University)

- Xin Zhang (Massachusetts Institute of Technology)

- Florian Zuleger (TU Wien)


- Hakjoo Oh (Korea University)


- Francois Bidet (LIX, École polytechnique)

- Liqian Chen (National University of Defense Technology)

- Mehmet Emre (University of California, Santa Barbara)

- John K. Feser (Massachusetts Institute of Technology)

- Kihong Heo (University of Pennsylvania)

- Maxime Jacquemin (LIX, École polytechnique)

- Sehun Jeong (Korea University)

- Matthieu Journault (Sorbonne Université)

- Yue Li (Aarhus University)

- Viktor Malik (Brno University of Technlogy)

- Suvam Mukherjee (Microsoft Research)

- Abdelraouf Ouadjaout (Sorbonne Université)

- Saswat Padhi (University of California, Los Angeles)

- Jiasi Shen (Massachusetts Institute of Technology)

- Gagandeep Singh (ETH Zurich)

- Benno Stein (University of Colorado Boulder)

- Yulei Sui (University of Technology Sydney)

- Tian Tan (Aarhus University)

- Xinyu Wang (University of Texas at Austin)