Abstract
Software testing is becoming more critical to ensure that software functions properly. As the time, effort, and funds invested in software testing activities have been increased significantly, these resources still cannot meet the increasing demand of software testing. Managers must allocate testing resources to the test cases effectively in uncovering important defects. This study builds a value function that can quantify the relative value of a test case and thus play a significant role in prioritizing test cases, addressing the resource constraint issues in software testing and serving as a foundation of AI for software testing. The authors conducted a Monte Carlo simulation to exhibit application of the final value function.
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