Adaptive Random Testing Through Iterative Partitioning

Chen, T. Y.; Huang, De; Zhi, Zhou

doi:10.1007/11767077_13

Cited by 33 publications

(34 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this paper, we consider but one of the ART family of algorithms, namely, Iterative Partitioning ART (IP-ART) [6]. The original IP-ART algorithm works on the real domain, and we revised the algorithm so that it works on the integer domain.…”

Section: An Algorithm Of Generating Priority Numbersmentioning

confidence: 99%

Similarity-Based Search for Model Checking: A Pilot Study with Java PathFinder

Ibrahimov

Wang

Zhi

2013

2013 13th International Conference on Quality Software

Self Cite

View full text Add to dashboard Cite

When a model checker cannot explore the entire state space because of limited resources, model checking becomes a kind of testing with an attempt to find a failure (violation of properties) quickly. We consider two state sequences in model checking: (i) the sequence in which new states are generated, and (ii) the sequence in which the states generated in sequence (i) are checked for property violation. We observe that neighboring states in sequence (i) often have similarities in certain ways. Based on this observation we propose a search strategy, which generates sequence (ii) in such a way that similar states are evenly spread over the sequence. As a result, neighboring states in sequence (ii) can have a higher diversity. A pilot empirical study with Java PathFinder suggests that the proposed strategy can outperform random search in terms of creating equal or smaller number of states to detect a failure. Abstract --When a model checker cannot explore the entire state space because of limited resources, model checking becomes a kind of testing with an attempt to find a failure (violation of properties) quickly. We consider two state sequences in model checking: (i) the sequence in which new states are generated, and (ii) the sequence in which the states generated in sequence (i) are checked for property violation. We observe that neighboring states in sequence (i) often have similarities in certain ways. Based on this observation we propose a search strategy, which generates sequence (ii) in such a way that similar states are evenly spread over the sequence. As a result, neighboring states in sequence (ii) can have a higher diversity. A pilot empirical study with Java PathFinder suggests that the proposed strategy can outperform random search in terms of creating equal or smaller number of states to detect a failure.

show abstract

Section: An Algorithm Of Generating Priority Numbersmentioning

confidence: 99%

Similarity-Based Search for Model Checking: A Pilot Study with Java PathFinder

Ibrahimov

Wang

Zhi

2013

2013 13th International Conference on Quality Software

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, Adaptive Random Testing has been proposed for common failure patterns in terms of test cases to detect the failure. However, in recent study [4] it has been found that the performance of a partition testing strategy depends not only on the failure rate, it also on the geometric pattern of the failure-causing inputs. The new type of random testing is developed as adaptive random testing which shows that the effectiveness of random testing also it can be improved without incurring significant overheads rather than ordinary random testing.…”

Section: Introductionmentioning

confidence: 99%

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Indhumathi¹,

Sarala²

2014

IJCA

View full text Add to dashboard Cite

Test case generation is a path to identify the solution in software testing. Adaptive random testing is an enhancement of random testing to improve the quality of fault-revealing. The research focuses on software adaptive random testing based on Matrix called Partitioned Block based Adaptive Random Testing. It compares the performance of PBART with the existing Adaptive random testing using random samples of test cases which are drawn from blocks of distinct partitions. Partition testing defines as a block of test cases partitioned into set of all test cases. Thereby it has prompted to investigate the performance of random testing that can be improved by taking the patterns of failure-causing inputs which utilizes the prior knowledge and the information of the test cases. The proposed algorithm PB -ART performs the testing of program structure and load the source code to matrix with scenarios, method flows and data values. In numerical experiments, the approach examines effectiveness of PB-ART with ordinary adaptive random testing. There exist three measures for evaluating the effectiveness of a testing technique namely P-measure, E-measure and Fmeasure. Moreover F-measure is intuitively more appealing to testers and more realistic and informative from a practical point of view. Therefore, F-measure is chosen for measuring testing techniques in this research work.

show abstract

“…접근이 가장 용이한 소프트웨어 테스트 자동화 방법의 하나인 랜덤 테스팅은 입력 도메인 내에서 임의의 값을 선택 하여 테스트 케이스를 생성하고, 이를 이용하여 주어진 소프 트웨어에 대한 테스팅을 수행하도록 한다. 이와 같은 랜덤 테스팅은 소프트웨어 테스팅 자동화를 위한 가장 기본적이고 중요한 방법의 하나로 [7], 다양한 어플리케이션에 성공적으로 적용되어왔다 [9,11]. 하지만 랜덤 테스팅은 테스트되는 소프 트웨어에 대한 정보를 전혀 이용할 수 없다는 제약을 가지기 때문에, Chen 등은 소프트웨어 내의 오류들이 특정한 형태를 이루는 경우, 테스트 케이스를 입력 도메인 내에 넓고 고르 게 분산시켜 순수 랜덤 테스팅에서보다 더 효율적으로 오류 를 찾아낼 수 있도록 하는 적응적 랜덤 테스팅(Adaptive Random Testing, ART)을 제안했고, 이를 기반으로 하는 다 양한 응용 기법들이 제안되어 왔다.…”

unclassified

“…하지만 랜덤 테스팅은 테스트되는 소프 트웨어에 대한 정보를 전혀 이용할 수 없다는 제약을 가지기 때문에, Chen 등은 소프트웨어 내의 오류들이 특정한 형태를 이루는 경우, 테스트 케이스를 입력 도메인 내에 넓고 고르 게 분산시켜 순수 랜덤 테스팅에서보다 더 효율적으로 오류 를 찾아낼 수 있도록 하는 적응적 랜덤 테스팅(Adaptive Random Testing, ART)을 제안했고, 이를 기반으로 하는 다 양한 응용 기법들이 제안되어 왔다. [4,[6][7][8][9][10].…”

unclassified

See 1 more Smart Citation

Adaptive Random Testing through Iterative Partitioning with Enlarged Input Domain

Shin¹,

Park²

2008

The KIPS Transactions:PartD

View full text Add to dashboard Cite

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

show abstract

Adaptive Random Testing Through Iterative Partitioning

Cited by 33 publications

References 22 publications

Similarity-Based Search for Model Checking: A Pilot Study with Java PathFinder

Similarity-Based Search for Model Checking: A Pilot Study with Java PathFinder

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Adaptive Random Testing through Iterative Partitioning with Enlarged Input Domain

Contact Info

Product

Resources

About