Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.

Pairwise feature-interaction testing for SPLs: potentials and limitations

Abstract: A fundamental problem of testing Software Product Lines (SPLs) is that variability enables the production of a large number of instances and it is difficult to construct and run test cases even for SPLs with a small number of variable features. Interacting features is a foundation of a fault model for SPLs, where faults are likely to be revealed at execution points where features exchange information with other features or influence one another. Therefore, a test adequacy criterion is to cover as many interactions among different features as possible, thus increasing the probability of finding bugs. Our approach combines a combinatorial designs algorithm for pairwise feature generation with model-based testing to reduce the size of the SPL required for comprehensive coverage of interacting features. We implemented our approach and applied it to an SPL from the automotive domain provided by one of our industrial partners. The results suggest that with our approach higher coverage of feature interactions is achieved at a fraction of cost when compared with a baseline approach of testing all feature interactions.

Active spectral clustering via iterative uncertainty reduction

Abstract: Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straight-forward in principle, as long as all pairwise similarities are available. However, in recent years, numerous examples have emerged in which the cost of assessing similarities is substantial or prohibitive. We propose an active learning algorithm for spectral clustering that incrementally measures only those similarities that are most likely to remove uncertainty in an intermediate clustering solution. In many applications, similarities are not only costly to compute, but also noisy. We extend our algorithm to maintain running estimates of the true similarities, as well as estimates of their accuracy. Using this information, the algorithm updates only those estimates which are relatively inaccurate and whose update would most likely remove clustering uncertainty. We compare our methods on several datasets, including a realistic example where similarities are expensive and noisy. The results show a significant improvement in performance compared to the alternatives.

Ceteris Paribus preference elicitation with predictive guarantees

Abstract: CP-networks have been proposed as a simple and intuitive graphical tool for representing conditional ceteris paribus preference statements over the values of a set of variables. While the problem of reasoning with CP-networks has been receiving some attention, there are very few works that address the problem of learning CP-networks. In this work we investigate the task of learning CP-networks, given access to a set of pairwise comparisons. We first prove that the learning problem is intractable, even under several simplifying assumptions. We then present an algorithm that, under certain assumptions about the observed pairwise comparisons, identifies a CP-network that entails these comparisons. We finally show that the proposed algorithm is a PAC-learner, and, thus, that the CP-networks it induces accurately predict the user's preferences on previously unseen situations.

Inferring Efficient Weights from Pairwise Comparison Matrices

Abstract: Several multi-criteria-decision-making methodologies assume the existence of weights associated with the different criteria, reflecting their relative importance.One of the most popular ways to infer such weights is the analytic hierarchy process, which constructs first a matrix of pairwise comparisons, from which weights are derived following one out of many existing procedures, such as the eigenvector method or the least (logarithmic) squares. Since different procedures yield different results (weights) we pose the problem of describing the set of weights obtained by “sensible” methods: those which are efficient for the (vector-) optimization problem of simultaneous minimization of discrepancies. A characterization of the set of efficient solutions is given, which enables us to assert that the least-logarithmic-squares solution is always efficient, whereas the (widely used) eigenvector solution is not, in some cases, efficient, thus its use in practice may be questionable.

An AHP Approach to Aircraft Selection Process

Abstract: In order to bring air travel demand and its capacity as closely together as possible, an airline needs to adopt an appropriate methodological approach for fleet planning process. The goal of this paper is to solve aircraft type(s) selection problem for known route network and forecasted air travel demand by using the Analytic Hierarchy Process (AHP). The sensitivity analysis of alternative ratings in respect to different pairwise comparisons of the criteria is carried out. By changing one element in the pairwise comparison matrix (while keeping others constant), the process of aircraft type selection is monitored hereby enabling possible improvements.