Paper Info
Title
SQL data generation to enhance search-based system testing
Abstract
ABSTRACTAutomated system test generation for web/enterprise systems requires either a sequence of actions on a GUI (e.g., clicking on HTML links), or direct HTTP calls when dealing with web services (e.g., REST and SOAP). However, web/enterprise systems do often interact with a database. To obtain higher coverage and find new faults, the state of the databases needs to be taken into account when generating white-box tests. In this work, we present a novel heuristic to enhance search-based software testing of web/enterprise systems, which takes into account the state of the accessed databases. Furthermore, we enable the generation of SQL data directly from the test cases. This is useful for when it is too difficult or time consuming to generate the right sequence of events to put the database in the right state. And it is also useful when dealing with databases that are ''read-only'' for the system under test, and the actual data is generated by other services. We implemented our technique as an extension of EvoMaster, where system tests are generated in the JUnit format. Experiments on five RESTful APIs show that our novel technique improves code coverage significantly (up to +18%).
Year
DOI
Venue
2019
10.1145/3321707.3321732
GECCO
Keywords
Field
DocType
SQL, database, SBST, automated test generation, system testing, REST, web service
SQL,Software engineering,Computer science,System testing,Artificial intelligence,Machine learning,Test data generation
Conference
Citations 
PageRank 
References 
2
0.38
0
Authors
2
Name
Order
Citations
PageRank
Andrea Arcuri1263092.48
Juan P. Galeotti21669.64