Vector Informatik in Germany has launched a software test tool for the development, test, and analysis of software in what it calls cyber physical embedded systems.
CANoe4SW supports software developers and testers in all markets throughout the entire development process of distributed systems and IoT devices. In virtual execution environments on the PC, in virtual machines or in the cloud, the tool helps developers achieve high-quality software at an early stage for any target operating system based on Windows or Linux.
The tool provides development, test, and analysis of software from a single component, a subsystem or the entire distributed system. Software developers and testers in all markets from medical and railway to automotive can use CANoe4SW along the entire development process of distributed systems and IoT devices, says the company. Virtual execution environments on the computer, in virtual machines, or in the cloud enable software to be tested for any target operating system based on Windows or Linux, e.g. Ubuntu, CentOS, SUSE.
CANoe4SW allows easy access to the System Under Test by its functional system interfaces at a pure software level. Testers can perform early “black-box” SIL testing independently of hardware availability. CANoe4SW integrates seamlessly in CI/CT environments. It also supports connectivity protocols such as MQTT to provide access to IoT devices and back end software running in the cloud.
Interactive development and test as well as simple automated testing helps to ensure quality of distributed systems. CANoe4SW supports testing at an early stage of the development process by using virtual execution environments, thereby significantly increasing the quality of the developed software. Complex systems become manageable by isolating individual components using models for the physical and software environments. Software developers use CANoe4SW to stimulate and monitor values over time, thereby also addressing dynamic aspects of the software under test.
This allows users to simulate error scenarios that would be difficult to implement in real environments, e.g. downtimes of cloud services. In contrast to