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EASE 2021
Mon 21 - Thu 24 June 2021
Mon 21 Jun 2021 15:55 - 16:15 at Zoom - Software Quality II Chair(s): Paolo Arcaini

Finding bugs in a commercial cyber-physical system (CPS) development tool such as Simulink is hard as its codebase contains millions of lines of code and complete formal language specifications are not available. While deep learning techniques promise to learn such language specifications from sample models, deep learning needs a large number of training data to work well. SLGPT addresses this problem by using transfer learning to leverage the powerful Generative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trained on a large set of training data. SLGPT adapts GPT-2 to Simulink with both randomly generated models and models mined from open-source repositories. SLGPT produced Simulink models that are both more similar to open-source models than its closest competitor, DeepFuzzSL, and found a super-set of the Simulink development toolchain bugs found by DeepFuzzSL.

Mon 21 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:15 - 16:15
Software Quality IIEASE 2021 / Vision and Emerging Results Track at Zoom
Chair(s): Paolo Arcaini National Institute of Informatics
On the Nature of Issues in Five Open Source Microservices Systems: An Empirical Study
EASE 2021
Muhammad Waseem Wuhan University, China, Peng Liang Wuhan University, Mojtaba Shahin Monash University, Aakash Ahmad , Ali Rezaei-Nasab
Pre-print Media Attached
DABT: A Dependency-aware Bug Triaging Method
EASE 2021
Hadi Jahanshahi Ryerson University, Kritika Chhabra Ryerson University, Mucahit Cevik Ryerson University, Ayşe Başar Ryerson University
Vision and Emerging Results
SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain
Vision and Emerging Results Track
Sohil Lal Shrestha The University of Texas at Arlington, Christoph Csallner University of Texas at Arlington
DOI Pre-print Media Attached