Simulink is MathWorks’ commercial cyber-physical systems development tool, which enables engineers to do rapid prototyping of their systems through simulation and embedded code generation. When a Simulink model meets requirements, engineers can utilize embedded coder to convert it into embedded code (e.g., C source code) and deploy it in safety-critical applications such as automotive, aerospace, and healthcare. However, bugs or incorrect implementations in code generation may lead to unexpected behaviors in target applications, posing security risks. Therefore, it is crucial to eliminate such bugs in embedded code generation. To address this issue, we propose DESCO, a differential testing approach to test embedded code generation in Simulink. DESCO considers the functional correlation between Simulink blocks for partitioning, aiming to generate diverse and complex bug-triggering Simulink models to thoroughly exercise the embedded code generation. DESCO then detects bugs by analyzing the outputs of these Simulink models by differential testing. The experiments demonstrate that DESCO significantly outperforms existing approaches. In three months, DESCO reported 16 issues, including 12 confirmed as bugs by MathWorks Support.

Simulink compiler testing is important since all cyber-physical system (CPS) models are required to be compiled by Simulink compiler. Current testing processes use CPS models generated by CPS model generators for testing. Since the effectiveness of CPS model generators heavily relies on suitable generator configurations, existing approaches randomize configurations or infer configurations with historical bug information to generate diverse bug-triggering CPS models. However, these approaches are designed for general-purpose compilers (e.g., GCC), which have two challenges when testing Simulink compiler, namely, the CPS model representation challenge on representing CPS models for diversity measurement and the configuration learning challenge on learning configurations to generate diverse CPS models. To address these challenges, we proposeReinforcement lEarning-basedCOnfiguRationDiversification (RECORD), a new configuration diversification approach. RECORD has a feature vectorization component, which addresses the first challenge by representing CPS models as feature vectors to capture the local and global characteristics of CPS models for diversity measurement. RECORD then uses a reinforcement learning component to generate diverse CPS models based on the learned relationship between configuration updates and diversity changes, thus addressing the second challenge. Experiments demonstrate that within three months, RECORD reported 11 confirmed Simulink compiler bugs, significantly outperforming the state-of-the-art configuration diversification approaches. RECORD can also facilitate different testing strategies to find more bugs.

Engineers frequently generate embedded code from Simulink models for control applications. However, target applications using the code could behave unexpectedly, due to the bugs in code generation. In this study, we propose MOPART, the first model partition based differential testing method for code generation testing in Simulink. MOPART uses multiple-way network partitioning to generate diverse bug-triggering Simulink models to thoroughly exercise the code generation process. MOPART then finds bugs by analyzing the outputs of these Simulink models with differential testing. Experiments show that MOPART significantly outperforms existing approaches, which finds 11 confirmed code generation bugs in only two weeks.

Based on the meteorological data sequence of Lanzhou city and circumjacent regions, and experimental data in urban area, data of climate factors were analyzed. The results are as follows: Temperature variation is divided into three stages in near 70 years; the early 1950's and the early 1980's are the jump points of Lanzhou urban climate during this period. The urban climate effect was analyzed by the difference of each climate factor between Lanzhou city and suburb. According to statistical data, three types of temperature differences show ascending trend from 1960 to 2000. These indicate that urban heat island effect becomes strong gradually. Annual precipitation difference decrease gradually from 1980, there is no rain island effect. Relative humidity in urban area declines gradually, and appears urban dry island effect from 1978. The overlap-add and weakening effects of mountain - valley breeze and heat island circumfluence affect wind speed in Lanzhou. It also displays that the thermodynamic property of urban underlying surface and the urban anthropologic heat have played an important role in urban climate effect.