@Article{cmc.2022.018718,
AUTHOR = {Yang Shi, Xiaoyu Song, Marek Perkowski, Fu Li},
TITLE = {Effectiveness Assessment of the Search-Based Statistical Structural Testing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {70},
YEAR = {2022},
NUMBER = {2},
PAGES = {2191--2207},
URL = {http://www.techscience.com/cmc/v70n2/44622},
ISSN = {1546-2226},
ABSTRACT = {Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to answer the following question: if we use a relaxed constraint that significantly reduces the time consumption on search, can the optimized input distribution still be effective in fault-detecting ability? In this article, we propose a type of criterion called fairness-enhanced-sum-of-triggering-probability (p-L1-Max). The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data generation. We conducted extensive experiments to compare the computation time and the fault-detecting ability between the two criteria. The result shows that the 1.0-L1-Max criterion has the highest efficiency, and it is more practical to use than the Tri-Low-Bound criterion. To measure a criterion’s fault-detecting ability, we introduce a definition of expected faults found in the effective test set size region. To measure the effective test set size region, we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition.},
DOI = {10.32604/cmc.2022.018718}
}