A Selection Method for Black Box Regression Testing with a Statistically Defined Quality Level

Improving System Reliability Assessment of Safety-Critical Systems using Machine Learning Optimization Techniques

“…This is also motivated by the fact that in the au-tomotive industry, up to 80% of system failures [1] that are detected during a regression test have not occurred previously. The reason behind this fact is that often many unintended bugs are introduced during a bug-fixing process.…”

“…So between two system releases many new unknown errors are often introduced. For reducing the overall test effort, we apply a test case selection method [1] based on hypothesis tests. Those test cases that are assumed to fail on their executions are accordingly selected.…”

“…This paper is an extension of work originally presented at the 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017) [1] and describes a methodology for efficiently assessing system safety. The focus of the paper is on regression testing of safety-critical systems consisting of black-box components.…”

“…1) as a new dimension for optimization is introduced. Thus [3] and [4] suggest that high dimensional optimization problems can be solved in reasonable timeframes by using evolutionary algorithms instead of a (grid)search-based approach as given in [1]. Accordingly, we propose an evolutionary optimization approach for increasing testing efficiency.…”

“…We extend the proposed selection method [1] by introducing suitable test case features that are used in the machine learning approach for increasing performance (see [2]). Therefore, each feature introduced increases the complexity of the optimization problem (cf.…”