The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets covera...The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements.This paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC coverage.The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation.At the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic al-gorithm.The threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the population.Finally,an improved crossover strategy is pro-posed to accelerate the convergence of the algorithm.The improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark programs.The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.展开更多
Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a b...Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.展开更多
文摘The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements.This paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC coverage.The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation.At the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic al-gorithm.The threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the population.Finally,an improved crossover strategy is pro-posed to accelerate the convergence of the algorithm.The improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark programs.The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.
基金Project supported by the National Key R&D Program of China(No.2017YFB1001802)the National Natural Science Foundation of China(Nos.61472440,61632015,61690203,and 61532007)。
文摘Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.
