28566545
A new heuristic for test suite reduction
Abstract A testing objective has to be defined in testing a program. A test suite is then constructed to satisfy the testing objective. The constructed test suite contains redundancy when some of its proper subsets can still satisfy the same testing objective. Since the costs of executing test cases and maintaining a test suite for regression testing may be expensive, the problem of test suite reduction arises. This paper proposes a heuristic towards the optimization of a test suite.
5643714
On Some Characterisation Problems of Subdomain Testing
Subdomain testing is a very general approach to the selection of test cases. It captures the characteristics of testing strategies that require the test suite to cover some predefined testing requirements. This paper attempts to characterise precisely the failure distributions for the best and worst case of any given subdomain testing strategy. Our analysis has revealed some crucial factors and principles that affect the effectiveness of subdomain testing strategies.
14782445
On the effectiveness of test case allocation schemes in partition testing
In partition testing, the input domain is divided into two or more disjoint subdomains according to some criteria and then test data are selected from each subdomain. Hence we must design a partitioning scheme that governs how to divide the input domain, and a test case allocation scheme that controls how to allocate test cases to the subdomains. Given any partitioning scheme, the test case allocation scheme plays a crucial part in the effectiveness of testing. This paper develops guidelines for comparing different test case allocation schemes. This provides solutions to the class of problems where various test case allocation schemes under the same partitioning scheme are compared for the effectiveness of testing. We illustrate with examples several potential applications of our findings.
12141135
Construction of classification trees via the classification-hierarchy table
Abstract The classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of classification trees. This paper supplements their studies by proposing a methodology to construct classification trees systematically from given sets of classifications and their associated classes, via the notion of the classification-hierarchy table. The intuition of the classification-hierarchy table is to capture the hierarchical relationship for every pair of distinct classifications from which classification trees can be constructed.
27503425
A More General Sufficient Condition for Partition Testing to be Better than Random Testing
Partition testing is an approach to program testing in which the input domain of the program is divided into partitions and test cases are selected from each partition. An alternative approach, known as random testing, is to select test cases at random from the entire input domain. Weyuker and Jeng (1991) observed that if all partitions are equal in sizes and the number of test cases selected from each partition is the same, then partition testing has a better chance of detecting at least one failure than random testing. This condition has been generalized by Chen and Yu (1994). They proved that partition testing is better than random testing so long as test cases are selected in proportion to the size of partitions. In this paper, we prove a more general sufficient condition. Partition testing performs better if the sampling rates are higher for partitions with higher failure rates. Some special cases that follow from this result are also considered.
13146679
A Broadcasting Algorithm in Star Graph Interconnection Networks
2083478
An integrative approach for causal gene identification and gene regulatory pathway inference
MOTIVATION
Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.


RESULTS
We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network.


AVAILABILITY
Source code is available upon request.
2301190
On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees
This paper addresses the informational asymmetry for constructing an ultrametric evolutionary tree from upper and lower bounds on pairwise distances between n given species. We show that the tallest ultrametric tree exists and can be constructed in O(n2) time, while the existence of the shortest ultrametric tree depends on whether the lower bounds are ultrametric. The tallest tree construction algorithm gives a very simple solution to the construction of an ultrametric tree. We also provide an efficient O(n2)-time algorithm for checking the uniqueness of an ultrametric tree, and study a query problem for testing whether an ultrametric tree satisfies both upper and lower bounds.
17905406
On the Markovian Randomized Strategy of Controller for Markov Decision Processes
This paper focuses on the so called controller synthesis problem, which addresses the question of how to limit the internal behavior of a given system implementation to meet its specification, regardless of the behavior enforced by the environment. We consider this problem in the probabilistic setting, where the underlying model has both probabilism and nondeterminism and the nondeterministic choices in some states are assumed to be controllable while the others are under the control of an unpredictable environment. As for the specification, it is defined by probabilistic computation tree logic. We show that under the restriction that the controller exploits only Markovian randomized strategy, the existence of such a controller is decidable, which is done by a reduction to the decidability of first-order theory for reals. This also gives rise to an algorithm which can synthesize the controller if it does exist.
6774619
Assessment of the reliability of protein-protein interactions and protein function prediction
As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex data. The supplementary information is available at the following Web site: http://www-hto.usc.edu/-msms/AssessInteraction/.