Paper Info
Title
Augmenting Field Data for Testing Systems Subject to Incremental Requirements Changes.
Abstract
When testing data processing systems, software engineers often use real-world data to perform system-level testing. However, in the presence of new data requirements, software engineers may no longer benefit from having real-world data with which to perform testing. Typically, new test inputs complying with the new requirements have to be manually written. We propose an automated model-based approach that combines data modelling and constraint solving to modify existing field data to generate test inputs for testing new data requirements. The approach scales in the presence of complex and structured data, thanks to both the reuse of existing field data and the adoption of an innovative input generation algorithm based on slicing the model into parts. We validated the scalability and effectiveness of the proposed approach using an industrial case study. The empirical study shows that the approach scales in the presence of large amounts of structured and complex data. The approach can produce, within a reasonable time, test input data that is over ten times larger in size than the data generated with constraint solving only. We also demonstrate that the generated test inputs achieve more code coverage than the test cases implemented by experienced software engineers.
Year
DOI
Venue
2017
10.1145/3053430
ACM Trans. Softw. Eng. Methodol.
Keywords
Field
DocType
System Testing,Data Processing Systems,Model Slicing,Alloy
Data mining,Data modeling,Test Management Approach,Systems engineering,Computer science,White-box testing,Test data,Data-driven testing,Computer engineering,Non-functional testing,Test data generation,Keyword-driven testing
Journal
Volume
Issue
ISSN
26
1
1049-331X
Citations 
PageRank 
References 
2
0.36
42
Authors
3
Name
Order
Citations
PageRank
Daniel Di Nardo1502.90
Fabrizio Pastore232923.60
Lionel C. Briand38795481.98