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The success of smartphones and app stores have contributed to the explosion of the number of mobile apps proposed to end-users. In this very competitive market, developers are rushed to regularly release new versions of their apps in order to retain users. Under such pressure, app developers may be tempted to adopt bad design or implementation choices, leading to the introduction of code smells. Mobile-specific code smells represent a real concern in mobile software engineering. Many studies have proposed tools to automatically detect their presence and quantify their impact on performance. However, there remains—so far—no evidence about the lifespan of these code smells in the history of mobile apps. In this paper, we present the first large-scale empirical study that investigates the survival of Android code smells. This study covers 8 types of Android code smells, 324 Android apps, 255k commits, and the history of 180k code smell instances. Our study reports that while in terms of time Android code smells can remain in the codebase for years before being removed, it only takes 34 effective commits to remove 75% of them. Also, Android code smells disappear faster in bigger projects with higher releasing trends. Finally, we observed that code smells that are detected and prioritised by linters tend to disappear before other code smells.
Index Terms-Mobile apps, Change history, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, Android, Mobile apps, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], code smells, [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing
Index Terms-Mobile apps, Change history, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, Android, Mobile apps, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], code smells, [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |