IMPROVING THE MEAN TIME BETWEEN FAILURE METRIC FOR ENSURING EFFECTIVE EVALUATION OF SOFTWARE TESTING RESULTS

Abstract

The mean time between failures (MTBF) metric is widely used in software testing to assess reliability and stability. However, traditional methods for calculating MTBF have certain drawbacks that reduce the effectiveness of evaluating testing results. The paper proposes ways to improve the MTBF metric method, including applying statistical methods, machine learning, software modeling, integrating with other metrics, automating calculations, and real-time monitoring. These approaches will increase the accuracy of MTBF calculation, account for interconnections between system components, and promptly respond to changes in reliability indicators. Implementing the proposed improvements to the MTBF method will promote more effective evaluation of testing results and increase overall software reliability.