Abstract
AbstractProduct-line architectures (PLAs) are an effective mechanism for facilitating the reuse of software components on different mobile devices. Mobile applications are typically delivered to devices using over-the-air provisioning services that allow a mobile phone to download and install software over a cellular network connection. Current techniques for automating product-line variant selection do not address the unique requirements (such as the need to consider resource constraints) of dynamically selecting a variant for overthe-air provisioning.This paper presents the following contributions to product-line variant selection for mobile devices: (1) it describes how a constraint solver can be used to dynamically select a product-line variant while adhering to resource constraints, (2) it presents architectures for automatically discovering device capabilities and mapping them to product-line feature models, (3) it includes results from experiments and field tests with an automated variant selector, and (4) it describes PLA design rules that can be used to increase the performance of automated constraint-based variant selection. Our empirical results show that fast automated variant selection from a feature model is possible if certain product-line design guidelines are followed.
Highlights
A recent trend in mobile devices that makes pervasive computing more realistic is the proliferation of services that allow mobile devices to download software on-demand across a mobile network
This paper presents the architecture and functionality of Scatter and provides the following contributions to research on software reuse for mobile devices: x We show how Scatter enables and disables features/components in product-line models based on the sets of device capabilities it receives from the provisioning server x We describe the automated variant selection engine, based on a Constraint Logic Programming Finite
Domain (CLP(FD)) solver [21, 37], that can dynamically derive a valid configuration of reusable software components suitable for a target device’s capabilities and resource constraints x We present data from experiments that show how Product-line architectures (PLAs) constraints impact variant selection time for a constraint-based variant selection engine x We describe PLA design rules gleaned from our experiments that help to improve variant selection time when using a constraint-based software reuse approach
Summary
A recent trend in mobile devices that makes pervasive computing more realistic is the proliferation of services that allow mobile devices to download software on-demand across a mobile network. Another missing detail of these automatic reuse approaches is the architecture for how an autonomous variant selection mechanism can be the integrated into an over-the-air provisioning server To address these gaps in online mobile software variant selection engines, we have developed a tool called Scatter that first captures the requirements of a PLA and the resources of a mobile device and quickly constructs a custom variant from a PLA for the device. The remainder of this paper is organized as follows: Section 2 presents the train food services application that we use as an example product-line throughout the paper; Section 3 describes the challenges of dynamically composing reusable software components for different mobile devices and the unresolved problems of using current techniques; Section 4 presents architectures for integrating an automated variant selection mechanism into an over-the-air provisioning server; Section 5 shows how Scatter automatically transforms PLA requirements and mobile device resources into a model that can be operated. On by the CLP(FD) based variant selector; Section 6 analyzes the results of field tests and simulations of using Scatter for over-the-air provisioning; Section 7 summarizes product-line design rules that we have learned from our results that improve the speed at which a product variant can be selected; Section 8 compares our work on Scatter with related research; and Section 9 presents lessons learned and concluding remarks
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