An empirical study of the performance impacts of Android code smells

Abstract

Android code smells are bad implementation practices within Android applications (or apps) that may lead to poor software quality, in particular in terms of performance. Yet, performance is a main software quality concern in the development of mobile apps. Correcting Android code smells is thus an important activity to increase the performance of mobile apps and to provide the best experience to mobile end-users while considering the limited constraints of mobile devices (e.g., CPU, memory, battery). However, no empirical study has assessed the positive performance impacts of correcting mobile code smells.In this paper, we therefore conduct an empirical study focusing on the individual and combined performance impacts of three Android performance code smells (namely, Internal Getter/Setter, Member Ignoring Method, and HashMap Usage) on two open source Android apps. To perform this study, we use the Paprika toolkit to detect these three code smells in the analyzed apps, and we derive four versions of the apps by correcting each detected smell independently, and all of them. Then, we evaluate the performance of each version on a common user scenario test. In particular, we evaluate the UI and memory performance using the following metrics: frame time, number of delayed frames, memory usage, and number of garbage collection calls. Our results show that correcting these Android code smells effectively improve the UI and memory performance. In particular, we observe an improvement up to 12.4% on UI metrics when correcting Member Ignoring Method and up to 3.6% on memory-related metrics when correcting the three Android code smells. We believe that developers can benefit from these results to guide their refactoring, and thus improve the quality of their mobile apps.

The Rise of Android Code Smells: Who is to Blame?

“…According to the guidelines of mobile frameworks, they hinder memory, CPU, and energy performances [3], [4], [36], [37]. This theoretical assumption has been confirmed by empirical studies that assessed the impact of mobile-specific code smells on app performances [9], [27], [39]. Hence, mobile-specific code smells represent "a gap between the current state of a software system and some hypothesized ideal state in which the system is optimally successful in a particular environment", which is a definition of technical debt [8].…”

“…Later, researchers proposed tools and approaches like PAPRIKA [28] and ADOC-TOR [38] to detect these code smells. Relying on these tools, empirical studies were conducted to prove the negative impact of mobile-specific code smells on app performance [9], [27], [39]. Other empirical studies focused on understanding the phenomenon of code smells in mobile apps.…”

On the Survival of Android Code Smells in the Wild

“…Other studies focused on assessing the performance impact of these code smells on app performance [11], [23], [44]. In particular, Palomba et al [44] showed that methods that represent a co-occurrence of Internal Setter, Leaking Thread, Member Ignoring Method, and Slow Loop, consume 87 times more energy than other smelly methods.…”

“…Mobile code smells are different from OO code smells as they often refer to a misuse of the platform SDK and they are more performanceoriented. This performance aspect drew the attention of the research community as many studies investigated the impact of these code smells on performances, demonstrating that their refactoring can significantly improve app performance [11], [23], [44]. Apart from performance, other aspects of mobilespecific code smells remained unaddressed.…”

A delta‐oriented approach to support the safe reuse of black‐box code rewriters

“…The list of these apps is available in Appendix in Table . We also took 19 rules that detect and correct when possible energy‐related antipatterns in Android apps . The experiments were run on a mid‐2015 MacBook Pro computer, with a 2,5 GHz Intel Core i7 processor and 16 GB 1600 MHz DDR3 of RAM.…”