Software failure prediction is an important activity during agile software development as it can help managers to identify the failure modules. Thus, it can reduce the test time, cost and assign testing resources efficiently. To ensure that the development of the software is likely to fail in a specific level, there are two techniques are used in this work, Support Vector Machine (SVM) to determine the factors leading to failure, and to define the dependent and independent variables the correlation coefficient (CC) has been used. RapidMiner Studio9.4 has been used to perform all the required steps from preparing the primary data to visualizing the results and evaluating the outputs, as well as verifying and improving them in a unified environment. Two datasets are used in this work, the results for the first one indicate that the percentage of failure to predict the time used in the test is for all 181 rows, for all test times recorded, is 3% for Mean time between failures (MTBF). Whereas, SVM achieved a 97% success in predicting compared to previous work whose results indicated that the use of Administrative Delay Time (ADT) achieved a statistically significant overall success rate of 93.5%. At the same time, the second dataset result indicates that the percentage of failure to predict the time used or experiment in the test is for all 1091 rows, for all test times recorded, is 1.5% for MTBF, SVM achieved 98.5% prediction. Keywords: Software Failure, Agile, Support Vector Machine, Correlation Coefficient