Constructing Test Suites for Real-Time Embedded Systems Under Input Timing Constraints

Abstract

Testing of real-time embedded systems (RTESs) under input timing constraints is a critical issue. Models which can specify timing constraints have respective merits and demerits and test suites which can cover more input possibilities and detect more faults under input timing constraints are worthy of study. In this paper, clocked computation tree logic which is used to specify input timing constraints is presented. Neighbor covering arrays and parallel input time correlation test suites are introduced to test RTESs under serial and parallel input timing constraints. Three algorithms are described in generating test suites, respectively, and corresponding random testing-based algorithms which are used as baselines for comparison are introduced. Benchmarks with different configurations are conducted to evaluate the algorithms' performance. Three real-world RTESs are tested with the test suites described in this paper, respectively. The test results show that random testing may omit some neighbor input time point combinations as the randomness and increase test suite scales. This fact may lead to the omission of some faults and heavy costs. Therefore, the proposed test suites are more effective and efficient for testing RTESs under input timing constraints. INDEX TERMS Real-time embedded systems, input timing constraints, test suite generation, random testing.

Forward-Propagation Approach for Generating Feasible and Minimum Test Case Suites From Cause-Effect Graph Specifications

“…Fault detection rate FDR can be calculated by using (5), where 𝐹 represents faults from Table XI, 𝑡 𝑗 represents tests contained in the desired test case suite and 𝑛 faults represents the total number of faults in the system (in this case study, the total number of faults is six). Maximum fault detection rate is 100% (in case when the test case suite detects all faults).…”

“…Cause-effect graphs are a popular black-box testing technique because logical relations represent Boolean mathematical principles that are easy to understand and intuitive to use [4], as well as because this technique can be applied to safety-critical systems which have rigorous testing requirements (such as missile navigation systems described in [5]). For these reasons, the cause-effect graphing technique has been continually enhanced and generalized to a larger set of problems since from the 1970s, when it was first introduced in [6], to this day.…”