Title
System-Level Test Case Prioritization Using Machine Learning
Abstract
Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.
Year
DOI
Venue
2016
10.1109/ICMLA.2016.0065
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
Keywords
Field
DocType
System-Level Testing,Black-Box Testing,Test Case Prioritization,Supervised Machine Learning
Test suite,Data mining,Test Management Approach,Computer science,Manual testing,Test script,Regression testing,Test case,Artificial intelligence,Test data generation,Machine learning,Keyword-driven testing
Conference
ISBN
Citations 
PageRank 
978-1-5090-6168-6
5
0.38
References 
Authors
13
5
Name
Order
Citations
PageRank
Remo Lachmann1443.86
Sandro Schulze225923.43
Manuel Nieke381.08
Christoph Seidl420720.15
Ina Schaefer5163499.16