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Challenges of Testing Machine Learning Applications

Volume 14, Number 6, June 2018, pp. 1275-1282
DOI: 10.23940/ijpe.18.06.p18.12751282

Song Huanga, Er-Hu Liub, Zhan-Wei Huia, Shi-Qi Tangb, and Suo-Juan Zhangb

aSoftware Testing and Evaluation Centre, Army Engineering University of PLA, Nanjing, 210001, China
bCommand & Control Engineering College, Army Engineering University of PLA, Nanjing, 210001, China

(Submitted on March 21, 2018; Revised on April 20, 2018; Accepted on May 16, 2018)

Abstract:

Machine learning applications have achieved impressive results in many areas and provided effective solution to deal with image recognition, automatic driven, voice processing etc. problems. As these applications are adopted by multiple critical areas, their reliability and robustness becomes more and more important. Software testing is a typical way to ensure the quality of applications. Approaches for testing machine learning applications are needed. This paper analyzes the characteristics of several machine learning algorithms and concludes the main challenges of testing machine learning applications. Then, multiple preliminary techniques are presented according to the challenges. Moreover, the paper demonstrates how these techniques can be used to solve the problems during the testing of machine learning applications.

 

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