Volume 9 Number 10 (Oct. 2014)
Home > Archive > 2014 > Volume 9 Number 10 (Oct. 2014) >
JSW 2014 Vol.9(10): 2514-2521 ISSN: 1796-217X
doi: 10.4304/jsw.9.10.2514-2521

Optimizing Test Case Execution Schedule using Classifiers

Xiang Chen, Zhaofei Tan, Jian Xia, Pengfei He
School of Computer Science and Technology, Nantong University, Nantong 226019, China

Abstract—As software evolves, test suite continually grows larger. However running all the test cases in the test suite is prohibitive in most cases. To reduce the cost of regression testing, we can optimize test case execution schedule to maximize the early fault detection rate of the original test suite. Different from previous research, we use classification algorithms to guide the schedule process based on code change information and running result analysis. In particular, we firstly train a classifier for each test case using both the code change information and the running result in previous versions. Then we secondly use the trained classifier to estimate the fault detection probability of the test case in a new version. Finally we generate a test case execution schedule report based on the fault detection probability of all the test cases. To verify the effectiveness of our approach, we performed an empirical study on Siemens Suite, which includes 7 real programs written by C programming language, and chose some typical classification algorithms, such as decision tree classifier, Bayes classifier, or nearest neighbor classifier. Based on the final result, we find that in most cases, our approach can outperform a random approach and then further provide a guideline for achieving cost-effective test case execution schedule when using our approach.

Index Terms—software testing, regression testing, test case execution schedule, classification algorithm, empirical study


Cite: Xiang Chen, Zhaofei Tan, Jian Xia, Pengfei He, "Optimizing Test Case Execution Schedule using Classifiers," Journal of Software vol. 9, no. 10, pp. 2514-2521, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
  • Apr 26, 2021 News!

    Vol 14, No 4- Vol 14, No 12 has been indexed by IET-(Inspec)     [Click]

  • Nov 18, 2021 News!

    Papers published in JSW Vol 16, No 1- Vol 16, No 6 have been indexed by DBLP   [Click]

  • Dec 24, 2021 News!

     Vol 15, No 1- Vol 15, No 6 has been indexed by IET-(Inspec)   [Click]

  • Nov 18, 2021 News!

    [CFP] 2022 the annual meeting of JSW Editorial Board, ICCSM 2022, will be held in Rome, Italy, July 21-23, 2022   [Click]

  • Feb 09, 2023 News!

    Vol 18, No 1 has been published with online version   [Click]