Thu 8 Nov 2018 13:30 - 13:52 at Horizons 5 - Probabilistic Reasoning Chair(s): Antonio Filieri

Program variables used in robotic and cyber-physical systems often have implicit physical units that cannot be determined from their variable types. Inferring an abstract physical unit type for variables and checking their physical unit type consistency is of particular importance for validating the correctness of such systems. For instance, a variable with the unit of ‘meter’ should not be assigned to another variable with the unit of ‘degree-per-second’. Existing solutions have various limitations such as requiring developers to annotate variables with physical units and only handling variables that are directly or transitively used in popular robotic libraries with known physical unit information. We observe that there are a lot of physical unit hints in these softwares such as variable names and specific forms of expressions. These hints have uncertainty as developers may not respect conventions. We propose to model them with probability distributions and conduct probabilistic inference. At the end, our technique produces a unit distribution for each variable. Unit inconsistencies can then be detected using the highly probable unit assignments. Experimental results on 30 programs show that our technique can infer units for 159.3% more variables compared to the state-of-the-art with more than 88.7% true positives, and inconsistencies detection on 90 programs shows that our technique reports 103.3% more inconsistencies with 85.3% true positives.

Thu 8 Nov
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13:30 - 15:00: Probabilistic ReasoningResearch Papers at Horizons 5
Chair(s): Antonio FilieriImperial College London
13:30 - 13:52
Talk
Phys: Probabilistic Physical Unit Assignment and Inconsistency Detection
Research Papers
Sayali KatePurdue University, USA, John-Paul OreUniversity of Nebraska-Lincoln, USA, Xiangyu ZhangPurdue University, Sebastian ElbaumUniversity of Nebraska-Lincoln, USA, Zhaogui XuNanjing University, China
Pre-print
13:52 - 14:14
Talk
Testing Probabilistic Programming Systems
Research Papers
Saikat DuttaUniversity of Illinois at Urbana-Champaign, USA, Owolabi LegunsenUniversity of Illinois at Urbana-Champaign, Zixin HuangUniversity of Illinois at Urbana-Champaign, USA, Sasa MisailovicUniversity of Illinois at Urbana-Champaign
14:14 - 14:36
Talk
Verifying the Long-Run Behavior of Probabilistic System Models in the Presence of Uncertainty
Research Papers
Yamilet R. Serrano LlerenaNational University of Singapore, Singapore, Marcel BöhmeMonash University, Marc Brüninknil, Singapore, Guoxin SuUniversity of Wollongong, Australia, David S. Rosenblum