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Machine Learning Frameworks for Automated Software Testing Tools : A Study

  • Kim, Jungho (Analysis & Consulting Team, Big Data Business Division, ONYCOM Inc.) ;
  • Ryu, Joung Woo (Analysis & Consulting Team, Big Data Business Division, ONYCOM Inc.) ;
  • Shin, Hyun-Jeong (School of IT Convergence Engineering/Computer Science & Engineering Shinhan University) ;
  • Song, Jin-Hee (School of IT Convergence Engineering/Computer Science & Engineering Shinhan University)
  • Received : 2017.01.31
  • Accepted : 2017.02.13
  • Published : 2017.03.28

Abstract

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

Acknowledgement

Supported by : Korea Evaluation Institute of Industrial Technology

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