Finite-state machines (FSMs) and the W method have been widely used in software testing. However, the W method fails to detect post-processing errors in the implementation under test (IUT) because it ends testing when it encounters a previously visited state. To alleviate this issue, we propose an enhanced fault-detection W method. The proposed method does not stop the test, even if it has reached a previously visited state; it continues to test and check the points that the W method misses. Through various case studies, we demonstrated software testing using the W method and the proposed method. From the results, it can be inferred that the proposed method can more explicitly determine the consistency between design and implementation, and it is a better option for testing larger software. Unfortunately, the testing time of the proposed method is approximately 1.4 times longer than that of the W method because of the added paths. However, our method is more appropriate than the W method for software testing in safety-critical systems, even if this method is time consuming. This is because the error-free characteristics of a safety-critical system are more important than anything else. As a result, our method can be used to increase software reliability in safety-critical embedded systems.

Wireless Sensor Networks (WSNs) can be implemented as one of sensor systems in Network Centric Warfare (NCW). Mobility support and energy efficiency are key concerns for this application, due to multiple mobile users and stimuli in real combat field. However, mobility support approaches that can be adopted in this circumstance are rare. This paper proposes Multiple Mobility Support Approach (MMSA) based on Probing Environment and Adaptive Sleeping (PEAS) to support the simultaneous mobility of both multiple users and stimuli by sharing the information of stimuli in WSNs. Simulations using Qualnet are conducted, showing that MMSA can support multiple mobile users and stimuli with good energy efficiency. It is expected that the proposed MMSA can be applied to real combat field.