Paper Info
Title
Seeding strategies in search-based unit test generation.
Abstract
Search-based techniques have been applied successfully to the task of generating unit tests for object-oriented software. However, as for any meta-heuristic search, the efficiency heavily depends on many factors; seeding, which refers to the use of previous related knowledge to help solve the testing problem at hand, is one such factor that may strongly influence this efficiency. This paper investigates different seeding strategies for unit test generation, in particular seeding of numerical and string constants derived statically and dynamically, seeding of type information and seeding of previously generated tests. To understand the effects of these seeding strategies, the results of a large empirical analysis carried out on a large collection of open-source projects from the SF110 corpus and the Apache Commons repository are reported. These experiments show with strong statistical confidence that, even for a testing tool already able to achieve high coverage, the use of appropriate seeding strategies can further improve performance. © 2016 The Authors. Software Testing, Verification and Reliability Published by John Wiley & Sons Ltd.
Year
DOI
Venue
2016
10.1002/stvr.1601
Softw. Test., Verif. Reliab.
Keywords
Field
DocType
test case generation,search-based testing,testing classes,search-based software engineering,JUnit,Java
Computer science,Statistical Confidence,Unit testing,Software,Artificial intelligence,Software testing,Simulation,Generating unit,Java,Reliability engineering,Seeding,Machine learning,Search-based software engineering
Journal
Volume
Issue
ISSN
26
5
0960-0833
Citations 
PageRank 
References 
20
0.66
19
Authors
3
Name
Order
Citations
PageRank
José Miguel Rojas120911.84
Gordon Fraser22625116.22
Andrea Arcuri3263092.48