Abstract: Memory leak is a common defect in continuous working software,such as cloud applications,web service,middleware,etc.It can affect the stability of software applications,lead to run in bad performance and even crash.To clearly observe memory leaks,the test cases toward them need to execute longer time in order to generate significant memory pressure.The cost of memory leaks testing is expensive.If the execution orders of test cases are not optimized,we may waste lots of time on the test cases that are not likely to reveal faults before finding test cases that really containing memory leaks.This seriously reduces the efficiency of fault discovery.In order to make up for the shortcomings of the existing technology and solve the problems of the me-mory leak of Java Web program while running for a long time,which is not easy to find,diagnose and repair,this paper studies the memory leak detection technology,proposes the memory leak test script prediction method based on machine learning.The methodtrains and predicts the script with memory leak by building the memory feature model.Then,based on the training model,it predicts the risk value of script memory leak,and gives the corresponding parameter scores,to guide the subsequent script reorganization,can predict and obtain the function test script that is more likely to cause memory leak.At the same time,a script reorganization optimization method is proposed to improve its defect revealing ability.Priority testing of predicted and recombined scripts can accelerate the detection of leakage defects.Finally,a case study shows that the proposed framework has strong leak detection ability.The speed of defect detection of the optimized test script can be more than twice as fast as that of the common script,thus accelerating the exposure time of memory expansion problem,achieving the purpose of improving test efficiency and ensuring software quality.

Key words: Machine learning, Memory leak, Memory leak prediction model, Test script, test script reconstruction