TEST CASES REDUCTION THROUGH PRIORITIZATION TECHNIQUE
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND INFORMATION TECHNOLOGY
Author(s): AVINASH GUPTA , DHARMENDER SINGH KUSHWAHA , DUSHYANT KUMAR SINGH , NAYANEESH MISHRA
Abstract: Regression testing is a costly and time taking affair. Because of time and resource constraints it is not possible to run all the test cases of the regression test suite. Prioritization of test cases provides a way to run highest priority test cases in the first phase. It helps to improve the percentage of fault detected in given time and is found to work better with feedback mechanism. History Based approach is one of the methods to prioritize the test cases based on the feedback of each test case. The feedback for each of the test cases is obtained from the history of each of the test cases in terms fault detection, number of executions and other such factors. This paper proposes the prioritization of test cases for the modified lines of code. The results establish that number of sessions required for running the test cases reduces by almost 33%.
- Publication Date: 02-Jun-2014
- DOI: 10.15224/978-1-63248-010-1-21
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EEG-BASED CLASSIFICATION OF IMAGINED FISTS MOVEMENTS USING MACHINE LEARNING AND WAVELET TRANSFORM ANALYSIS
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND INFORMATION TECHNOLOGY
Author(s): ALI M. BANIYOUNES , EMAD A. AWADA , MOHAMMAD H. ALOMARI
Abstract: Electroencephalography (EEG) signals represent the brain activity by the electrical voltage fluctuations along the scalp. In this paper, we propose a system that enables the differentiation between imagined left or right fist movements for the purpose of controlling computer applications via imagination of fist movements. EEG signals were filtered and processed using a hybrid system that uses wavelet transform analysis and machine learning algorithms. Many Daubechies orthogonal wavelets were used to analyze the extracted events. Then, the Root Mean Square (RMS) and the Mean Absolute Value (MAV) were calculated to the wavelet coefficients in two detail levels. Support Vector Machines (SVMs) and Neural Networks (NNs) were applied to the feature vectors and optimized by carrying out an intensive learning and testing experiments. Optimum classification performances of 84.5% and 82.1% were obtained with SVMs and NNs, respectively. Compared with the related research work reported in the lite
- Publication Date: 02-Jun-2014
- DOI: 10.15224/978-1-63248-010-1-22
- Views: 0
- Downloads: 0