Deterministic Graph-Walking Program Mining

M.K.: Parallel program performance prediction using deterministic task graph analysis

Complementing deterministic tree-walking automata

We consider various kinds of deterministic tree-walking automata, with and without pebbles, over ranked and unranked trees. For each such kind of automata we show that there is an equivalent one which never loops. The main consequence of this result is the closure under complementation of the various types of automata we consider with a focus on the number of pebbles used in order to complement...

Fault Localization via Mining Program Dynamic Execution Graph ⋆

This paper is concerned with the problem of locating the code area related to software potential fault quickly and accurately in software testing period. A new method Sig BB based on graph model is proposed for mining the suspicious fault nodes from the passing and failing execution graphs. Representing each execution of a program as a graph, the graphs are divided into the passing and failing ...

Deterministic vs Non-deterministic Graph Property Testing

A graph property P is said to be testable if one can check whether a graph is close or far from satisfying P using few random local inspections. Property P is said to be non-deterministically testable if one can supply a “certificate” to the fact that a graph satisfies P so that once the certificate is given its correctness can be tested. The notion of non-deterministic testing of graph propert...

Omni Graph Mining: Graph mining using RDBMS

In this study, we compare three approaches to implement graph algorithms SQL, MapReduce, and GraphChi in terms of performance and cost efficiency. Since MapReduce was introduced, it has been considered as a standard tool to analyze large scale data including graph data. However, we believe that there is a ‘sweet spot’ in which RDBMS can outperform MapReduce. We think that the performance of the...