Embedded software often interacts with multiple inputs from various sensors whose dependency is often complex or partially known to developers. With incomplete information on dependency, testing is likely to be insufficient in detecting errors. We propose a method to enhance testing coverage of embedded software by identifying subtle and often neglected dependencies using information contained in usage log. Usage log, traditionally used primarily for investigative purpose following accidents, can also make useful contribution during testing of embedded software. Our approach relies on first individually developing behavioral model for each environmental input, performing compositional analysis while identifying feasible but untested dependencies from usage log, and generating additional test cases that correspond to untested or insufficiently tested dependencies. Experimental evaluation was performed on an Android application named Gravity Screen as well as an Arduino-based wearable glove app. Whereas conventional CTM-based testing technique achieved average branch coverage of 26% and 68% on these applications, respectively, proposed technique achieved 100% coverage in both.

Embedded software developers assume the behavior of the environment when specifications are not available. However, developers may assume the behavior incorrectly, which may result in critical faults in the system. Therefore, it is important to detect the faults caused by incorrect assumptions. In this letter, we propose a log-based testing approach to detect the faults. First, we create a UML behavioral model to represent the assumed behavior of the environment, which is then transformed into a state model. Next, we extract the actual behavior of the environment from a log, which is then incorporated in the state model, resulting in a state model that represents both assumed and actual behaviors. Existing testing techniques based on the state model can be used to generate test cases from our state model to detect faults.