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Android is a highly fragmented platform with a diverse set of devices and users. To support the deployment of apps in such a heterogeneous setting, Android has introduceddynamic delivery—a new model of software deployment in which optional, device- or user-specific functionalities of an app, calledDynamic Feature Modules (DFMs), can be installed, as needed, after the app’s initial installation. This model of app deployment, however, has exacerbated the challenges of properly testing Android apps. In this article, we first describe the results of an extensive study in which we formalized a defect model representing the various conditions under which DFM installations may fail. We then presentDeltaDroid—a tool aimed at assisting the developers with validating dynamic delivery behavior in their apps by augmenting their existing test suite. Our experimental evaluation using real-world apps corroboratesDeltaDroid’s ability to detect many crashes and unexpected behaviors that the existing automated testing tools cannot reveal.
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A Deployment Tool for Large Scale Graph Analytics Framework Arachne
Data sets have grown exponentially in size, rapidly surpassing the scale at which traditional exploratory data analysis (EDA) tools can be used effectively to analyze real-world graphs. This led to the development of Arachne, a user-friendly tool enabling interactive graph analysis at terabyte scales while using familiar Python code and utilizing a high-performance back-end powered by Chapel that can be run on nearly any *nix-like system. Various disciplines, including biological, information, and social sciences, use large-scale graphs to represent the flow of information through a cell, connections between neurons, interactions between computers, relationships between individuals, etc. To take advantage of Arachne, however, a new user has to go through a long and convoluted installation process, which often takes a week or more to complete, even with assistance from the developers. To support Arachne’s mission of being an easy-to-use exploratory graph analytics tool that increases accessibility to high performance computing (HPC) resources, a better deployment experience was needed for users and developers. In this paper, we propose a tool specially designed to greatly simplify the deployment of Arachne for users and offer the ability to rapidly and automatically test the software for compatibility with new releases of its dependencies. The highly portable nature of Arachne necessitates that this deployment tool be able to install and configure the software in diverse combinations of hardware, operating system, initial system environment, and the evolving packages and libraries in Arachne. The tool was tested in both virtual and real-world environments, where its success was evaluated by an improvement to efficiency and productivity by both users and developers. Current results show that the installation and configuration process was greatly improved, with a significant reduction in the time and effort spent by both users and developers.
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- Award ID(s):
- 2109988
- PAR ID:
- 10561972
- Publisher / Repository:
- 28th Annual IEEE High Performance Extreme Computing Conference (HPEC)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Virtual
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
