Real-time Finite Fault Rupture Detector (FinDer) for large earthquakes

To provide rapid estimates of fault rupture extent during large earthquakes, we have developed the Finite Fault Rupture Detector algorithm, ‘FinDer’. FinDer uses image recognition techniques to detect automatically surface-projected fault ruptures in real-time (assuming a line source) by estimating their current centroid position, length L, and strike θ. The approach is based on a rapid high-frequency near/far-source classification of ground motion amplitudes in a dense seismic network (station spacing <50 km), and comparison with a set of pre-calculated templates using ‘Matching by Correlation’. To increase computational efficiency, we perform the correlation in the wavenumber domain. FinDer keeps track of the current dimensions of a rupture in progress. Errors in L are typically on the same order as station spacing in the network. The continuously updated estimates of source geometries as provided by FinDer make predicted shaking intensities more accurate and thus more useful for earthquake early warning, ShakeMaps, and related products. The applicability of the algorithm is demonstrated for several recorded and simulated earthquakes with different focal mechanisms, including the 2009 M_w 6.3 L’Aquila (Italy), the 1999 M_w 7.6 ChiChi (Taiwan) and the M_w 7.8 ShakeOut scenario earthquake on the southern San Andreas Fault (California)

Combining Spreadsheet Smells for Improved Fault Prediction

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.Comment: 4 pages, 1 figure, to be published in 40th International Conference on Software Engineering: New Ideas and Emerging Results Trac

Mutation Testing as a Safety Net for Test Code Refactoring

Refactoring is an activity that improves the internal structure of the code without altering its external behavior. When performed on the production code, the tests can be used to verify that the external behavior of the production code is preserved. However, when the refactoring is performed on test code, there is no safety net that assures that the external behavior of the test code is preserved. In this paper, we propose to adopt mutation testing as a means to verify if the behavior of the test code is preserved after refactoring. Moreover, we also show how this approach can be used to identify the part of the test code which is improperly refactored