Effectiveness Assessment of the Search-Based Statistical Structural Testing

作     者：Yang Shi Xiaoyu Song Marek Perkowski Fu Li 

作者机构：Department of Electrical and Computer EngineeringPortland State UniversityPortland97201USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：2191-2207页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：Publication of this article in an open access journal was funded by the Portland State University Library’s Open Access Fund 

主　　题：Statistical structural testing evolutionary algorithms optimization coverage criteria 

摘      要：Search-based statistical structural testing(SBSST)is a promising technique that uses automated search to construct input distributions for statistical structural *** has been proved that a simple search algorithm,for example,the hill-climber is able to optimize an input ***,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 *** input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation ***-Low-Bound is considered a strong criterion,and it is demonstrated to sustain a high fault-detecting *** 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 faultdetecting ability?In this article,we propose a type of criterion called fairnessenhanced-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 *** conducted extensive experiments to compare the computation time and the fault-detecting ability between the two *** 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 *** measure a criterion’s fault-detecting ability,we introduce a definition of expected faults found in the effective test set size *** 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.