Abstract:
The utility of a smartphone is limited by the battery capacity and software ability to efficiently use its battery. To properly characterize an app's energy consumption and identify energy defects, it is critical to test the apps properly. Currently, for energy testing, developers rely on tests intended for assessing the functional correctness of apps. But, are such tests adequate for revealing energy defects in apps? If not, how can we automatically generate such tests and determine whether such tests can reveal energy defects? Answers to these inquiries are the subject of my research, and in this talk, I will focus on the last two questions.
The first part of the talk presents COBWEB, a search-based evolutionary technique to automatically generate energy tests. Experimental results on real-world Android apps demonstrate not only COBWEB's ability to test the energy behavior of apps effectively and efficiently, but also its superiority over state-of-the-art and state-of-practice techniques finding a broader and more diverse set of energy defects. The second part of the talk presents ACETON, a technique that employs Deep Learning to automatically construct an oracle that not only determines whether a test execution reveals an energy defect but also the type of energy defects. The experimental results show that the oracle produced by ACETON is highly accurate and efficient in practice.
Bio:
Reyhaneh is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She obtained my Ph.D. from the University of California at Irvine. She has been awarded the Google PhD Fellowship in Programing Technology and Software Engineering for her work on advancing energy testing of Android and has been recognized as a Rising Star in EECS.
Part of the Illinois Computer Science Speakers Series. Faculty Host: Darko Marinov.