Elimination algorithms for data flow analysis

Region Analysis: A Parallel Elimination Method for Data Flow Analysis

| Parallel data ow analysis methods ooer the promise of calculating detailed semantic information about a program at compile-time more eeciently than sequential techniques. Previous work on parallel elimination methods 1, 2] has been hampered by the lack of control over interval size; this can prohibit eeective parallel execution of these methods. To overcome this problem, we have designed the ...

Average Case Analysis of Some Elimination-Based Data-Flow Analysis Algorithms

The average case of some elimination-based data-flow analysis algorithms is analyzed in a mathematical way. Besides this allows for comparing the timing behavior of the algorithms, it also provides insights into how relevant the underlying statistics are when compared to practical settings.

Financial Reporting Fraud Detection: An Analysis of Data Mining Algorithms

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

Elimination of Unstructured Loops in Flow Analysis

Generating Data Flow Analysis Algorithms from Modal Specifications

The paper develops a framework that is based on the idea that modal logic provides an appropriate framework for the speciication of data ow analysis (DFA) algorithms as soon as programs are represented as models of the logic. This can be exploited to construct a DFA-generator that generates eecient implementations of DFA-algorithms from modal speciications by partially evaluating a speciic mode...