John Howroyd

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 scalable approach to conditioned slicing

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 ConSUS prunes the symbolic execution paths, and its predicate reasoning system uses the FermaT simplify transformation in place of a more conventional theorem prover We show that this combination of pruning and simplification-as-reasoner leads to a more scalable approach to conditioning.

Equivalence of conservative, free, linear program schemas is decidable

A program schema defines a class of programs, all of which have identical statement structures, but whose expressions may differ. We prove that given any two structured schemas which are conservative, linear and free, it is decidable whether they are equivalent.

A formal relationship between program slicing and partial evaluation

A formal relationship between program slicing and partial evaluation is established. It is proved that for terminating programs, a residual program produced by partial evaluation is semantically equivalent to a conditioned slice.

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

Probabilistic record linkage has been well investigated in recent 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 naturally 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

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

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ConSUS: a light-weight program conditioner

scores.

Static Program Slicing Algorithms are Minimal for Free Liberal Program Schemas

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.