NDLI: Search-based multi-paths test data generation for structure-oriented testing

Content Provider	ACM Digital Library
Author	Cao, Yang Li, Luming Hu, Chunhua
Abstract	This paper presents a new fitness function to generate test data for a specific single path, which is different from the predicate distance applied by most test data generators based on genetic algorithms (GAs). We define a similarity between the target path and execution path to evaluate the quality of the populations. The problem of the most existing generators is to search only one target data a time, wasting plenty of available interim data. We construct another fitness function combined with the single path function, which can drive GA to complete covering multi-paths to avoid the reduplicate searching and utilize the interim populations for different paths. Several experiments are taken to examine the effectiveness of both the single path and multi-path fitness functions, which evaluate the functions' performance with the convergence ability and consumed time. Results show that the two functions perform well compared with other two typical path-oriented functions and the multi-paths approach retrenches the searching actually.
Starting Page	25
Ending Page	32
Page Count	8
File Format	PDF
ISBN	9781605583266
DOI	10.1145/1543834.1543839
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Software testing Automation test Path testing Structure-oriented Genetic algorithms
Content Type	Text
Resource Type	Article

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Search-based multi-paths test data generation for structure-oriented testing