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The Incremental Maintenance of Transition Tour

Németh G. A., Pap Z.

Fundamenta Informaticae

2014

Vol. 129, nr 3

279--300

While evolutionary developmentmethodologies have become increasingly prevalent, incremental testing methods are lagging behind. Most traditional test generation algorithms – including the Transition Tour method – rebuild test sequences from scratch even if minimal changes to the system have been made. In the current paper we propose two incremental algorithms to update a Transition Tour test sequence after changes in a deterministic finite state machine model. Our solution uses existing information – the Eulerian graph of a previous version of the system and an Euler tour in it – to update the test cases of the system in response to modification. The first algorithm keeps an Eulerian graph up to date, while the second algorithm maintains an Euler tour in the augmented graph. Analytical and practical analyses show that our algorithms are very efficient in the case of changing specifications. We also demonstrate our methods through an example.

Rough Rule Extracting From Various Conditions: Incremental and Approximate Approaches for Inconsistent Data

Liu Y., Xu C., Zhang Q, Pan Y.

Fundamenta Informaticae

2008

Vol. 84, nr 3-4

403-427

Rough rule extraction refers to the rule induction method by using rough set theory. Although rough set theory is a powerful mathematical tool in dealing with vagueness and uncertainty in data sets, it is lack of effective rule extracting approach under complex conditions. This paper proposes several algorithms to perform rough rule extraction from data sets with different properties. Firstly, in order to obtain uncertainty rules from inconsistent data, we introduce the concept of confidence factor into the rule extracting process. Then, an improved incremental rule extracting algorithm is proposed based on the analysis of the incremental data categories. Finally, above algorithms are further extended to perform approximate rule extraction from huge data sets. Preliminary experiment results are encouraging.

Efficient Implementation of the Italiano Algorithms for Updating the Transitive Closure on Associative Parallel Processors

Nepomniaschay A.

Fundamenta Informaticae

2008

Vol. 89, nr 2-3

313-329

We propose a simple data structure for an efficient implementation of the Italiano algorithms for the dynamic updating of the transitive closure of a directed graph represented as adjacency matrix on a model of associative (or content addressable) parallel processors with vertical processing (the STAR–machine). Associative versions of the Italiano algorithms are represented as procedures DeleteArc1 and InsertArc1. We prove the correctness of these procedures and evaluate their time and space complexity. We also present the main advantages of associative versions of the Italiano algorithms.