Abstract This study presents a parallel version of the string matching algorithms research tool (SMART) library, implemented on NVIDIA’s compute unified device architecture (CUDA) platform, and uses general-purpose computing on graphics processing unit (GPGPU) programming concepts to enhance performance and gain insight into the parallel versions of these algorithms. We have developed the CUDA-enhanced SMART (CUSMART) library, which incorporates parallelized iterations of 64 string matching algorithms, leveraging the CUDA application programming interface. The performance of these algorithms has been assessed across various scenarios to ensure a comprehensive and impartial comparison, allowing for the identification of their strengths and weaknesses in specific application contexts. We have explored and established optimization techniques to gauge their influence on the performance of these algorithms. The results of this study highlight the potential of GPGPU computing in string matching applications through the scalability of algorithms, suggesting significant performance improvements. Furthermore, we have identified the best and worst performing algorithms in various scenarios.

Abstract With the increasing use of battery‐powered devices comes the need to test mobile applications for energy consumption and energy issues. Unfortunately, energy testing is expensive because it is a manual, labor‐intensive process that often requires multiple, separate, energy‐measuring devices to collect energy usage data. The high costs of energy testing can negatively affect the planning process of application evolution. For example, developers might be limited in the number of changes they can include in a release because they must conservatively plan to conduct energy testing after each change. In this paper, we present a new approach to provide developers with feedback on executing/skipping energy tests for proposed code changes. Our technique leverages change impact analysis and precomputed API energy usage information. More specifically, for a proposed change, the technique predicts whether energy testing will be required, and if so, which energy tests will need to be run. Such information may allow developers to avoid spending unnecessary time for energy testing and develop an effective application evolution timeline. To investigate the feasibility of our technique, we implemented a prototype for Android applications and conducted three case studies at different granularity levels on 10 Android applications.

Abstract Software piracy is an important concern for application developers. Such concerns are especially relevant in mobile application development, where piracy rates can be greater than 90%. The most common approach used by mobile developers to prevent piracy is code obfuscation. However, the decision to apply such transformations is currently made without regard to the impacts of obfuscations on another area of increasing concern for mobile application developers, energy usage. Because both software piracy and battery life are important concerns, mobile application developers must strike a balance between protecting their applications and preserving the battery lives of their users' devices. To help them make such choices, we conducted an empirical study of the effects of 18 code obfuscations on the amount of energy consumed by executing a total of 21 usage scenarios spread across 11 Android applications on four different mobile phone platforms. The results of the study indicate that, while obfuscations can have a statistically significant impact on energy usage and are more likely to increase energy usage than to decrease energy usage, the magnitudes of such impacts are unlikely to be meaningful to mobile application users. Copyright © 2016 John Wiley & Sons, Ltd.

Context: Code refactoring's benefits to understandability, maintainability and extensibility are well known enough that automated support for refactoring is now common in IDEs. However, the decision to apply such transformations is currently performed without regard to the impacts of the refactorings on energy consumption. This is primarily due to a lack of information and tools to provide such relevant information to developers. Unfortunately, concerns about energy efficiency are rapidly becoming a high priority concern in many environments, including embedded systems, laptops, mobile devices, and data centers.

Software piracy is an important concern for application developers. Such concerns are especially relevant in mobile application development, where piracy rates can approach 90%. The most commonly used approach by mobile developers for preventing piracy is code obfuscation. However, the decision to apply such transformations is currently made without regard to the impacts of obfuscations on another area of increasing concern for mobile application developers: energy consumption. Because both software piracy and battery life are important concerns, mobile application developers must strike a balance between protecting their applications and preserving the battery lives of their users' devices. To help them make such choices, we conducted an empirical study of the effects of 18 code obfuscations on the amount of energy consumed by executing a total of 15 usage scenarios spread across 11 Android applications. The results of the study indicate that, while obfuscations can have a statistically significant impact on energy usage and are significantly more likely to increase energy usage than to decrease energy usage, the magnitudes of such impacts are unlikely to impact mobile application users.

In situ testing techniques have become an important means of ensuring the reliability of embedded systems after they are deployed in the field. However, these techniques do not help testers optimize the energy consumption of their in situ test suites, which can needlessly waste the limited battery power of these systems. In this work, we extend prior techniques for test suite minimization in such a way as to allow testers to generate energy-efficient, minimized test suites with only minimal modifications to their existing work flow. We perform an extensive empirical evaluation of our approach using the test suites provided for real world applications. The results of the evaluation show that our technique is effective at generating, in less than one second, test suites that consume up to 95% less energy while maintaining coverage of the testing requirements.