KDFC-ART: a KD-tree approach to enhancing Fixed-size-Candidate-set Adaptive Random Testing

<p>Testing and verification of digital circuits is of vital importance in electronics industry. Moreover, key designs require preservation of their intellectual property that might restrict access to the internal structure of circuit under test. Random testing is a classical solution to black box testing as it generates test patterns without using the structural implementation of the circuit under test. However, random testing ignores the importance of previously applied test patterns while generating subsequent test patterns. An improvement to random testing is Antirandom that diversifies every subsequent test pattern in the test sequence. Whereas, computational intensive process of distance calculation restricts its scalability for large input circuit under test. Fixed sized candidate set adaptive random testing uses predetermined number of patterns for distance calculations to avoid computational complexity. A combination of max-min distance with previously executed patterns is carried out for each test pattern candidate. However, the reduction in computational complexity reduces the effectiveness of test set in terms of fault coverage. This paper uses a total cartesian distance based approach on fixed sized candidate set to enhance diversity in test sequence. The proposed approach has a two way effect on the test pattern generation as it lowers the computational intensity along with enhancement in the fault coverage. Fault simulation results on ISCAS’85 and ISCAS’89 benchmark circuits show that fault coverage of the proposed method increases up to 20.22% compared to previous method.</p>

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An Enhanced Adaptive Random Testing by Dividing Dimensions Independently

Mathematical Problems in Engineering ◽

10.1155/2019/9381728 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Zhibo Li ◽

Qingbao Li ◽

Lei Yu

Keyword(s):

Boundary Effect ◽

Real Life ◽

Failure Detection ◽

Test Case Generation ◽

Test Case ◽

Test Cases ◽

Random Testing ◽

Failure Rates ◽

Adaptive Random Testing ◽

Candidate Set

Random testing (RT) is widely applied in the area of software testing due to its advantages such as simplicity, unbiasedness, and easy implementation. Adaptive random testing (ART) enhances RT. It improves the effectiveness of RT by distributing test cases as evenly as possible. Fixed Size Candidate Set (FSCS) is one of the most well-known ART algorithms. Its high failure-detection effectiveness only shows at low failure rates in low-dimensional spaces. In order to solve this problem, the boundary effect of the test case distribution is analyzed, and the FSCS algorithm of a limited candidate set (LCS-FSCS) is proposed. By utilizing the information gathered from success test cases (no failure-causing test inputs), a tabu generation domain of candidate test case is produced. This tabu generation domain is eliminated from the current candidate test case generation domain. Finally, the number of test cases at the boundary is reduced by constraining the candidate test case generation domain. The boundary effect is effectively relieved, and the distribution of test cases is more even. The results of the simulation experiment show that the failure-detection effectiveness of LCS-FSCS is significantly improved in high-dimensional spaces. Meanwhile, the failure-detection effectiveness is also improved for high failure rates and the gap of failure-detection effectiveness between different failure rates is narrowed. The results of an experiment conducted on some real-life programs show that LCS-FSCS is less effective than FSCS only when the failure distribution is concentrated on the boundary. In general, the effectiveness of LCS-FSCS is higher than that of FSCS.

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