- author = "Carlos Eduardo {De Jesus Guimaraes Oliveira}",
- title = "Mapping the Effectiveness of Automated Test Suite Generation Techniques",
- school = "Faculty of Information Technology, Monash University",
- year = "2019",
- address = "Australia",
- month = "22 " # sep,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://bridges.monash.edu/articles/thesis/Mapping_the_Effectiveness_of_Automated_Test_Suite_Generation_Techniques/9918029",
-
URL = "https://bridges.monash.edu/ndownloader/files/17837528",
-
DOI = "
doi:10.26180/5d9d60fe04121",
- size = "167 pages",
-
abstract = "Automated Test Suite Generation (ATSG) is an important
topic in Software Engineering, with a wide range of
techniques and tools being used in academia and
industry. While their usefulness is widely recognized,
due to the labour-intensive nature of the task, the
effectiveness of the different techniques in
automatically generating test cases for different
software systems is not thoroughly understood. Despite
many studies introducing various ATSG techniques
(ATSGT), much remains to be learned, however, about
what makes a particular technique work well (or not)
for a specific software system.
In this thesis, we seek an answer to the question What features of a software system impact the effectiveness of ATSGTs? Once these features are identified, can they be used to select the most effective ATSGT for a particular software system? To this end, we have implemented the META tool (Mapping the Effectiveness of Test Automation), a new framework that identifies important software features that can be used to select suitable ATSGTs to apply to new software systems. META is an alternative to the current methodology used to assess ATSGT performance.",
-
notes = "p79 'we use a Genetic Programming (GP) algorithm to
evolve branch predicates that are easy and hard for
different fitness function.'
Principal supervisor: Aldeida Aleti, Additional supervisor 1: Yuan-Fang Li, Additional supervisor 2: Kate Smith-Miles",
