Paper Info
Title
Testing variability-intensive systems using automated analysis: an application to Android
Abstract
Software product lines are used to develop a set of software products that, while being different, share a common set of features. Feature models are used as a compact representation of all the products (e.g., possible configurations) of the product line. The number of products that a feature model encodes may grow exponentially with the number of features. This increases the cost of testing the products within a product line. Some proposals deal with this problem by reducing the testing space using different techniques. However, a daunting challenge is to explore how the cost and value of test cases can be modeled and optimized in order to have lower-cost testing processes. In this paper, we present TESting vAriAbiLity Intensive Systems (TESALIA), an approach that uses automated analysis of feature models to optimize the testing of variability-intensive systems. We model test value and cost as feature attributes, and then we use a constraint satisfaction solver to prune, prioritize and package product line tests complementing prior work in the software product line testing literature. A prototype implementation of TESALIA is used for validation in an Android example showing the benefits of maximizing the mobile market share (the value function) while meeting a budgetary constraint.
Year
DOI
Venue
2016
10.1007/s11219-014-9258-y
Software Quality Journal
Keywords
Field
DocType
Testing,Software product lines,Automated analysis,Feature models,Android
Black-box testing,Data mining,Computer science,Manual testing,Non-regression testing,Regression testing,White-box testing,Software performance testing,Feature model,Software product line,Reliability engineering
Journal
Volume
Issue
ISSN
24
2
0963-9314
Citations 
PageRank 
References 
9
0.45
46
Authors
4
Name
Order
Citations
PageRank
Jose A. Galindo124821.10
Hamilton A. Turner21026.95
David Benavides343630.52
Jules White415213.93