An upper bound on the weight-balanced testing procedure with multiple testers

This paper presents the performance of the Weight-Balanced Testing (WBT) algorithm with multiple testers. The WBT algorithm aims to minimize the expected number of (round of) tests and has been proposed for coding, memory storage, search and testing applications. It often provides reasonable results if used with a single tester. Yet, the performance of the WBT algorithm with multiple testers and particularly its upper bound have not been previously analyzed, despite the large body of literature that exists on the WBT algorithm, and the recent papers that suggest its use in various testing applications. Here we demonstrate that WBT algorithm with multiple testers is far from being the optimal search procedure. The main result of this paper is the generalization of the upper bound on the expected number of tests previously obtained for a single-tester WBT algorithm. For this purpose, we first draw an analogy between the WBT algorithm and alphabetic codes; both being represented by the same Q-ary search tree. The upper bound is then obtained on the expected path length of a Q-ary tree, which is constructed by the WBT algorithm. Applications to the field of testing and some numerical examples are presented for illustrative purposes.

[1]  R. Ahlswede,et al.  Search Problems , 1987 .

[2]  T. C. Hu,et al.  Optimal Computer Search Trees and Variable-Length Alphabetical Codes , 1971 .

[3]  Marc J. Lipman,et al.  Minimum average cost testing for partially ordered components , 1995, IEEE Trans. Inf. Theory.

[4]  Arne Andersson General Balanced Trees , 1999, J. Algorithms.

[5]  D. Du,et al.  Combinatorial Group Testing and Its Applications , 1993 .

[6]  Tzvi Raz,et al.  Self-correcting inspection procedure under inspection errors , 2002 .

[7]  D. Huffman A Method for the Construction of Minimum-Redundancy Codes , 1952 .

[8]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[9]  Lev B. Levitin,et al.  An application of information theory and error-correcting codes to fractional factorial experiments , 2001 .

[10]  Derick Wood,et al.  A Top-Down Updating Algorithm for Weight-Balanced Trees , 1993, Int. J. Found. Comput. Sci..

[11]  Jeffrey Scott Vitter,et al.  Online Data Structures in External Memory , 1999, WADS.

[12]  J. Abrahams,et al.  Minimum Average Cost Testing for Partially Ordered Components , 1993, Proceedings. IEEE International Symposium on Information Theory.

[13]  David A. Huffman,et al.  A method for the construction of minimum-redundancy codes , 1952, Proceedings of the IRE.

[14]  David C. van Voorhis,et al.  Optimal source codes for geometrically distributed integer alphabets (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[15]  Yasuichi Horibe An Improved Bound for Weight-Balanced Tree , 1977, Inf. Control..

[16]  Yigal Gerchak,et al.  OPTIMAL INSPECTION ORDER WHEN PROCESS’ FAILURE RATE IS CONSTANT , 1996 .

[17]  Tzvi Raz,et al.  Optimal Parallel Inspection for Finding the First Nonconforming Unit in a Batch--An Information Theoretic Approach , 2000 .

[18]  J. Abrahams Parallelized Huffman and Hu-Tucker searching , 1994, IEEE Trans. Inf. Theory.

[19]  Raymond W. Yeung Alphabetic codes revisited , 1991, IEEE Trans. Inf. Theory.

[20]  Pramod K. Varshney,et al.  Application of Information Theory to Sequential Fault Diagnosis , 1982, IEEE Transactions on Computers.

[21]  Tzvi Raz Information theoretic measures of inspection performance , 1991 .

[22]  Kurt Mehlhorn,et al.  On the Average Number of Rebalancing Operations in Weight-Balanced Trees , 1980, Theor. Comput. Sci..