Call for Participation: FormaliSE 2020 (www.formalise.org)
Virtual conference on Formal Methods in Software Engineering (July 13, 2020)
Co-located event of ICSE 2020
INTRODUCTION
FormaliSE is a yearly conference on Formal Methods in Software Engineering.
FormaliSE is organized by FME (Formal Methods Europe) and is co-located with
ICSE (International Conference on Software Engineering). The main goal of the
conference is to foster integration between the formal methods and the software
engineering communities. The lack of formalization in key places makes software
engineering overly sensitive to the weaknesses that are inevitable in the
complex activities behind software creation. This is where formal methods (FMs)
have a huge opportunity.
PROGRAM
This year FormaliSE will be held virtually. There will be synchronous tracks and
asynchronous ones. The asynchronous tracks feature pre-recorded presentations by
the authors of each of the 14 accepted papers. They are exclusively available to
participants on YouTube from 29 June onwards.
Then, on July 13, we will have two synchronous "live" sessions hosted on Zoom:
1) 07:00-09:00 UTC with a keynote by Shahar Maoz
2) 15:00-17:00 UTC with a keynote by Corina Pasareanu
After each keynote, there will be some time for a live interaction between attendees
and authors of accepted papers, who will be able to answer and discuss the questions
asked on sli.do/Slack. These sessions will also be recorded and made available to
you, so if you can not attend both sessions since the timing is inconvenient, then
you can watch them afterwards as well.
See https://www.formalise.org/program for a detailed overview of
keynotes and the list of accepted papers.
REGISTRATION
Registration for FormaliSE is open from 10 June onwards. You can register at
https://2020.icse-conferences.org/attending/registration. Registration is
handled by ICSE. Fees are approximately 25 USD for all (one of the authors of each
accepted papers pays 150 USD). The fee entitles you to view the prerecorded paper
presentations and to participate in the synchronous events on 13 July.
ORGANISATION
Nico Plat and Stefania Gnesi (General Chairs)
Kyungmin Bae and Domenico Bianculli (PC Chairs)
KEYNOTE SPEAKERS
1) Shahar Maoz is an Associate Professor at the School of Computer
Science in Tel Aviv University, where he heads the Software Modeling
Laboratory. Shahar has a BSc and MSc computer science degrees from Tel
Aviv University, and a PhD from the Weizmann Institute. From 2010 to
2012 he was post-doc research fellow in RWTH Aachen University,
Germany, with a postdoctoral fellowship from the Minerva
Foundation. In 2015-2016 he spent a sabbatical at MIT CSAIL. Shahar's
research interests are in software engineering, specifically in the
use of models and formal methods for software evolution, model
inference, testing, and synthesis. His work has been published in top
software engineering and modeling conferences and journals. He is a
recipient of an ERC Starting Grant for the development of synthesis
technologies for reactive systems software engineers (project
SYNTECH).
Title of the keynote: SYNTECH: Synthesis Technologies for Reactive
Systems Software Engineers
ABSTRACT: Reactive synthesis is an automated procedure to obtain a
correct-by-construction reactive system from a given declarative,
temporal specification. Examples of these systems include the software
controllers of robotic systems. Despite recent advancements on the
theory and algorithms of reactive synthesis, e.g., efficient synthesis
for the GR(1) fragment of linear temporal logic, many challenges
remain in bringing reactive synthesis technologies to the hands of
software engineers. The SYNTECH project is about bridging this gap. It
addresses challenges that relate to the change from writing code to
writing specifications, and the development of tools to support a
specification-centric rather than a code-centric software development
process. In this talk I will give an overview of the SYNTECH
project’s results from the last five years. These include the Spectra
specification language and Spectra Tools, a synthesizer and related
analyses aiming at helping engineers write better specifications for
synthesis. I will also present the application of Spectra to classic
problems as well as to autonomous Lego robots and some example
simulated systems, as developed by undergraduate computer science
students in project classes we have taught. Finally, I will discuss
new challenges and research opportunities.
The talk will cover results from papers in ESEC/FSE’15, ESEC/FSE’16,
ESEC/FSE’17, ICSE’19, and FM'19. Joint work with Gal Amram,
Elizabeth Firman, Aviv Kuvent, Or Pistiner, Jan O. Ringert, and Rafi
Shalom. The project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020
research and innovation programme (grant agreement No 638049,
SYNTECH). For more information, see
http://smlab.cs.tau.ac.il/syntech/.
2) Corina Pasareanu is an Associate Research Professor with CyLab at
Carnegie Mellon University, working at the Silicon Valley campus with
NASA Ames Research Center. Her research interests include: model
checking and automated testing, compositional verification,
model-based development, probabilistic software analysis, and autonomy
and Security. She is the recipient of several awards, including ASE
Most Influential Paper Award (2018), ESEC/FSE Test of Time Award
(2018), ISSTA Retrospective Impact Paper Award (2018), ACM
Distinguished Scientist (2016), ACM Impact Paper Award (2010), ICSE
2010 Most Influential Paper Award (2010).
Title of the keynote: On the Probabilistic Analysis of Neural Networks
ABSTRACT: Neural networks are powerful tools for automated
decision-making, seeing increased application in safety-critical
domains, such as autonomous driving. Due to their black-box nature and
large scale, reasoning about their behavior is
challenging. Statistical analysis is often used to infer probabilistic
properties of a network, such as its robustness to noise and
inaccurate inputs. While scalable, statistical methods can only
provide probabilistic guarantees on the quality of their results and
may underestimate the impact of low probability inputs leading to
undesired behavior of the network.
We investigate here the use of symbolic analysis and constraint
solution space quantification to precisely quantify probabilistic
properties in neural networks. We demonstrate the potential of the
proposed technique in a case study involving the analysis of ACAS-Xu,
a collision avoidance system for unmanned aircraft control.
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