Model-based robustness testing for avionics-embedded software

Robustness testing for safety-critical embedded software is still a challenge in its nascent stages. In this paper, we propose a practical methodology and implement an environment by employing model-based robustness testing for embedded software systems. It is a system-level black-box testing approach in which the fault behaviors of embedded software is triggered with the aid of modelbased fault injection by the support of an executable model-driven hardware-in-loop （HIL） testing environment. The prototype implementation of the robustness testing environment based on the proposed approach is experimentally discussed and illustrated by industrial case studies based on several avionics-embedded software systems. The results show that our proposed and implemented robustness testing method and environment are effective to find more bugs, and reduce burdens of testing engineers to enhance efficiency of testing tasks, especially for testing complex embedded systems.

A TTCN-3-based protocol testing system and its extension

The protocol testing technology used in the next generation Internet should satisfy some new challenges and requirements. This paper focuses on the test suite description and test implementation techniques. TTCN-3 is chosen as the test suite description language and extended in both syntax and semantics to satisfy the requirements of protocol robustness testing. PITSv3, a protocol integrated testing system based on TTCN-3, is developed, and the extensions for robustness testing are implemented. Finally, two practical test applications are presented.

Testing hypotheses under covariate-adaptive randomisation andadditive models

Covariate-adaptive randomisation has a long history of applications in clinical trials. Shao, Yu,and Zhong [(2010). A theory for testing hypotheses under covariate-adaptive randomization.Biometrika, 97, 347–360] and Shao and Yu [(2013). Validity of tests under covariate-adaptivebiased coin randomization and generalized linear models. Biometrics, 69, 960–969] showed thatthe simple t-test is conservative under covariate-adaptive biased coin (CABC) randomisation interms of type I error, and proposed a valid test using the bootstrap. Under a general additivemodel with CABC randomisation, we construct a calibrated t-test that shares the same propertyas the bootstrap method in Shao et al. (2010), but do not need large computation required by thebootstrap method. Some simulation results are presented to show the finite sample performanceof the calibrated t-test.

A Brain-inspired SLAM System Based on ORB Features

This paper describes a brain-inspired simultaneous localization and mapping （SLAM） system using oriented features from accelerated segment test and rotated binary robust independent elementary （ORB） features of RGB （red, green, blue） sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RCB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.