Neural Network-Based Test Case Prioritization in Continuous Integration

Abstract

In continuous integration environments, the execution of test cases is performed for every newly added feature or when a bug fix occurs. Therefore, regression testing is performed considering various testing strategies. The Test Case Prioritization (TCP) approach considers reordering test cases so that faults are found earlier with a minimum execution cost. The purpose of the paper is to investigate the impact of neural network-based classification models to assist in the prioritization of test cases. Three different models are employed with various features (duration, fault rate, cycles count, total runs count) and considering information at every 30 cycles or at every 100 cycles. The results obtained emphasize that the NEUTRON approach finds a better prioritization with respect to NAPFD (normalized average percent of the detected fault) than random permutation and is comparable with the solutions that used either duration or faults, considering that it combines both values. Compared to other existing approaches, NEUTRON obtains similar com-petitive results when considering a budget of 50% and the best results when considering budgets of 75% and 100%.