The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform

Jian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • generating minimal test Set Satisfying mc dc criterion via sat based approach
    ACM Symposium on Applied Computing, 2018
    Co-Authors: Ling Yang, Jian Zhang
    Abstract:

    The Modified Condition/Decision Coverage (MC/DC) is a test criterion proposed by NASA to measure the test Set adequacy, especially in safety-critical systems. Existing works on test case generation for this criterion usually make use of greedy or meta-heuristic search techniques to construct a small test Set. This paper studies the problem of generating optimal test Set Satisfying this criterion. It enumerates the size of test Set from the theoretic lower-bound m + 1 to upper-bound 2m where m is the number of conditions that can be MC/DC covered, and then employs a SAT solver to decide whether there exists a test Set with a given size, until a test Set is found. We apply this method to benchmarks with no more than 25 conditions including dozens of instances collected from related works and millions of randomly generated ones. The experimental results indicate that most (more than 99%) of these instances can be MC/DC covered by a test Set with only m + 1 test cases. Therefore, our SAT-based approach is efficient for generating optimal MC/DC test Sets, since for most of the instances, it only invokes the SAT solver just once in the process of searching for minimal test Set.

  • SAC - Generating minimal test Set Satisfying MC/DC criterion via SAT based approach
    Proceedings of the 33rd Annual ACM Symposium on Applied Computing - SAC '18, 2018
    Co-Authors: Ling Yang, Jian Zhang
    Abstract:

    The Modified Condition/Decision Coverage (MC/DC) is a test criterion proposed by NASA to measure the test Set adequacy, especially in safety-critical systems. Existing works on test case generation for this criterion usually make use of greedy or meta-heuristic search techniques to construct a small test Set. This paper studies the problem of generating optimal test Set Satisfying this criterion. It enumerates the size of test Set from the theoretic lower-bound m + 1 to upper-bound 2m where m is the number of conditions that can be MC/DC covered, and then employs a SAT solver to decide whether there exists a test Set with a given size, until a test Set is found. We apply this method to benchmarks with no more than 25 conditions including dozens of instances collected from related works and millions of randomly generated ones. The experimental results indicate that most (more than 99%) of these instances can be MC/DC covered by a test Set with only m + 1 test cases. Therefore, our SAT-based approach is efficient for generating optimal MC/DC test Sets, since for most of the instances, it only invokes the SAT solver just once in the process of searching for minimal test Set.

Ling Yang - One of the best experts on this subject based on the ideXlab platform.

  • generating minimal test Set Satisfying mc dc criterion via sat based approach
    ACM Symposium on Applied Computing, 2018
    Co-Authors: Ling Yang, Jian Zhang
    Abstract:

    The Modified Condition/Decision Coverage (MC/DC) is a test criterion proposed by NASA to measure the test Set adequacy, especially in safety-critical systems. Existing works on test case generation for this criterion usually make use of greedy or meta-heuristic search techniques to construct a small test Set. This paper studies the problem of generating optimal test Set Satisfying this criterion. It enumerates the size of test Set from the theoretic lower-bound m + 1 to upper-bound 2m where m is the number of conditions that can be MC/DC covered, and then employs a SAT solver to decide whether there exists a test Set with a given size, until a test Set is found. We apply this method to benchmarks with no more than 25 conditions including dozens of instances collected from related works and millions of randomly generated ones. The experimental results indicate that most (more than 99%) of these instances can be MC/DC covered by a test Set with only m + 1 test cases. Therefore, our SAT-based approach is efficient for generating optimal MC/DC test Sets, since for most of the instances, it only invokes the SAT solver just once in the process of searching for minimal test Set.

  • SAC - Generating minimal test Set Satisfying MC/DC criterion via SAT based approach
    Proceedings of the 33rd Annual ACM Symposium on Applied Computing - SAC '18, 2018
    Co-Authors: Ling Yang, Jian Zhang
    Abstract:

    The Modified Condition/Decision Coverage (MC/DC) is a test criterion proposed by NASA to measure the test Set adequacy, especially in safety-critical systems. Existing works on test case generation for this criterion usually make use of greedy or meta-heuristic search techniques to construct a small test Set. This paper studies the problem of generating optimal test Set Satisfying this criterion. It enumerates the size of test Set from the theoretic lower-bound m + 1 to upper-bound 2m where m is the number of conditions that can be MC/DC covered, and then employs a SAT solver to decide whether there exists a test Set with a given size, until a test Set is found. We apply this method to benchmarks with no more than 25 conditions including dozens of instances collected from related works and millions of randomly generated ones. The experimental results indicate that most (more than 99%) of these instances can be MC/DC covered by a test Set with only m + 1 test cases. Therefore, our SAT-based approach is efficient for generating optimal MC/DC test Sets, since for most of the instances, it only invokes the SAT solver just once in the process of searching for minimal test Set.

Zuoling Zhou - One of the best experts on this subject based on the ideXlab platform.

Li Feng - One of the best experts on this subject based on the ideXlab platform.

Lars Ehlers - One of the best experts on this subject based on the ideXlab platform.

  • Von Neumann-Morgenstern stable Sets in matching problems
    Journal of Economic Theory, 2007
    Co-Authors: Lars Ehlers
    Abstract:

    The following properties of the core of a one well-known: (i) the core is non-empty; (ii) the core is a lattice; and (iii) the Set of unmatched agents is identical for any two matchings belonging to the core. The literature on two-sided matching focuses almost exclusively on the core and studies extensively its properties. Our main result is the following characterization of (von Neumann-Morgenstern) stable Sets in one-to-one matching problem only if it is a maximal Set Satisfying the following properties : (a) the core is a subSet of the Set; (b) the Set is a lattice; (c) the Set of unmatched agents is identical for any two matchings belonging to the Set. Furthermore, a Set is a stable Set if it is the unique maximal Set Satisfying properties (a), (b) and (c). We also show that our main result does not extend from one-to-one matching problems to many-to-one matching problems.(This abstract was borrowed from another version of this item.)

  • Von Neumann-Morgenstern Stable Sets in Matching Problems
    2005
    Co-Authors: Lars Ehlers
    Abstract:

    The following properties of the core of a one well-known: (i) the core is non-empty; (ii) the core is a lattice; and (iii) the Set of unmatched agents is identical for any two matchings belonging to the core. The literature on two-sided matching focuses almost exclusively on the core and studies extensively its properties. Our main result is the following characterization of (von Neumann-Morgenstern) stable Sets in one-to-one matching problem only if it is a maximal Set Satisfying the following properties : (a) the core is a subSet of the Set; (b) the Set is a lattice; (c) the Set of unmatched agents is identical for any two matchings belonging to the Set. Furthermore, a Set is a stable Set if it is the unique maximal Set Satisfying properties (a), (b) and (c). We also show that our main result does not extend from one-to-one matching problems to many-to-one matching problems.