Pre/post conditioned slicing

Th paper shows how analysis of programs in terms of pre- and postconditions can be improved using a generalisation of conditioned program slicing called pre/post conditioned slicing. Such conditions play an important role in program comprehension, reuse, verification and reengineering. Fully automated analysis is impossible because of the inherent undecidability of pre- and post- conditions. The method presented reformulates the problem to circumvent this. The reformulation is constructed so that programs which respect the pre- and post-conditions applied to them have empty slices. For those which do not respect the conditions, the slice contains statements which could potentially break the conditions. This separates the automatable part of the analysis from the human analysis

ConSUS: A light-weight program conditioner

Program conditioning consists of identifying and removing a set of statements which cannot be executed when a condition of interest holds at some point in a program. It has been applied to problems in maintenance, testing, re-use and re-engineering. All current approaches to program conditioning rely upon both symbolic execution and reasoning about symbolic predicates. The reasoning can be performed by a ‘heavy duty’ theorem prover but this may impose unrealistic performance constraints. This paper reports on a lightweight approach to theorem proving using the FermaT Simplify decision procedure. This is used as a component to ConSUS, a program conditioning system for the Wide Spectrum Language WSL. The paper describes the symbolic execution algorithm used by ConSUS, which prunes as it conditions. The paper also provides empirical evidence that conditioning produces a significant reduction in program size and, although exponential in the worst case, the conditioning system has low degree polynomial behaviour in many cases, thereby making it scalable to unit level applications of program conditioning

Slicing Sets and Measures, and the Dimension of Exceptional Parameters

We consider the problem of slicing a compact metric space \Omega with sets of the form \pi_{\lambda}^{-1}\{t\}, where the mappings \pi_{\lambda} \colon \Omega \to \R, \lambda \in \R, are \emph{generalized projections}, introduced by Yuval Peres and Wilhelm Schlag in 2000. The basic question is: assuming that \Omega has Hausdorff dimension strictly greater than one, what is the dimension of the 'typical' slice \pi_{\lambda}^{-1}{t}, as the parameters \lambda and t vary. In the special case of the mappings \pi_{\lambda} being orthogonal projections restricted to a compact set \Omega \subset \R^{2}, the problem dates back to a 1954 paper by Marstrand: he proved that for almost every \lambda there exist positively many $t \in \R$ such that \dim \pi_{\lambda}^{-1}{t} = \dim \Omega - 1. For generalized projections, the same result was obtained 50 years later by J\"arvenp\"a\"a, J\"arvenp\"a\"a and Niemel\"a. In this paper, we improve the previously existing estimates by replacing the phrase 'almost all \lambda' with a sharp bound for the dimension of the exceptional parameters.Comment: 31 pages, three figures; several typos corrected and large parts of the third section rewritten in v3; to appear in J. Geom. Ana

Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage

Probabilistic record linkage has been well investigated in re- cent years. The Fellegi-Sunter probabilistic record linkage and its enhanced version are commonly used methods, which calculate match and non-match weights for each pair of corresponding fields of record-pairs. Bayesian network classifiers – naive Bayes classifier and TAN have also been successfully used here. Very recently, an extended version of TAN (called ETAN) has been developed and proved superior in classification accuracy to conventional TAN. However, no previous work has applied ETAN in record linkage and investigated the benefits of using a nat rally existing hierarchy feature level information. In this work, we extend the naive Bayes classifier with such information. Finally we apply all the methods to four datasets and estimate the F1 scores

Entity Search/Match in Relational Databases

We study an entity search/match problem that requires retrieved tuples match to an input entity query. We assume the input queries are of the same type as the tuples in a materialised relational table. Existing keyword search over relational databases focuses on assembling tuples from a variety of relational tables in order to respond to a keyword query. The entity queries in this work differ from the keyword queries in two ways: (i) an entity query roughly refers to an entity that contains a number of attribute values, i.e. a product entity or an address entity; (ii) there might be redundant or incorrect information in the entity queries that could lead to misinterpretations of the queries. In this paper, we propose a transformation that first converts an unstructured entity query into a multi-valued structured query, and two retrieval methods are proposed to generate a set of candidate tuples from the database. The retrieval methods essentially formulate SQL queries against the database given the multi-valued structured query. The results of a comprehensive evaluation of a large-scale database (more than 29 millions tuples) and two real-world datasets showed that our methods have a good trade-off between generating correct candidates and the retrieval time compared to baseline approaches

Sixty Years of Fractal Projections

Sixty years ago, John Marstrand published a paper which, among other things, relates the Hausdorff dimension of a plane set to the dimensions of its orthogonal projections onto lines. For many years, the paper attracted very little attention. However, over the past 30 years, Marstrand's projection theorems have become the prototype for many results in fractal geometry with numerous variants and applications and they continue to motivate leading research.Comment: Submitted to proceedings of Fractals and Stochastics

Improving Record Linkage Accuracy with Hierarchical Feature Level Information and Parsed Data

Probabilistic record linkage is a well established topic in the literature. Fellegi-Sunter probabilistic record linkage and its enhanced versions are commonly used methods, which calculate match and non- match weights for each pair of records. Bayesian network classifiers – naive Bayes classifier and TAN have also been successfully used here. Recently, an extended version of TAN (called ETAN) has been developed and proved superior in classification accuracy to conventional TAN. However, no previous work has applied ETAN to record linkage and investigated the benefits of using naturally existing hierarchical feature level information and parsed fields of the datasets. In this work, we ex- tend the naive Bayes classifier with such hierarchical feature level information. Finally we illustrate the benefits of our method over previously proposed methods on 4 datasets in terms of the linkage performance (F1 score). We also show the results can be further improved by evaluating the benefit provided by additionally parsing the fields of these datasets

A Scalable Approach to Conditioned Slicing Abstract

Conditioned slicing can be applied to reverse engineering problems which involve the extraction of executable fragments of code in the context of some criteria of interest. This paper introduces ConSUS, a conditioner for the Wide Spectrum Language, WSL. The symbolic executor of Con-SUS prunes the symbolic execution paths, and its predicate reasoning system uses the FermaTsimplify transformation in place of a more conventional theorem prover. We show that this combination of pruning and simplificationas-reasoner leads to a more scalable approach to conditioning.