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Empirical Software Engineering
Erschienen in: Empirical Software Engineering 4/2020

04.05.2020

Characterizing the evolution of statically-detectable performance issues of Android apps

verfasst von: Teerath Das, Massimiliano Di Penta, Ivano Malavolta

Erschienen in: Empirical Software Engineering | Ausgabe 4/2020

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Abstract

Mobile apps are playing a major role in our everyday life, and they are tending to become more and more complex and resource demanding. Because of that, performance issues may occur, disrupting the user experience or, even worse, preventing an effective use of the app. Ultimately, such problems can cause bad reviews and influence the app success. Developers deal with performance issues thorough dynamic analysis, i.e., performance testing and profiler tools, albeit static analysis tools can be a valid, relatively inexpensive complement for the early detection of some such issues. This paper empirically investigates how potential performance issues identified by a popular static analysis tool — Android Lint — are actually resolved in 316 open source Android apps among 724 apps we analyzed. More specifically, the study traces the issues detected by Android Lint since their introduction until they resolved, with the aim of studying (i) the overall evolution of performance issues in apps, (ii) the proportion of issues being resolved, as well as (iii) the distribution of their survival time, and (iv) the extent to which issue resolution are documented by developers in commit messages. Results indicate how some issues, especially related to the lack of resource recycle, tend to be more frequent than others. Also, while some issues, primarily of algorithmic nature, tend to be resolved quickly through well-known patterns, others tend to stay in the app longer, or not to be resolved at all. Finally, we found how only 10% of the issue resolution is documented in commit messages.
Vorheriger Artikel What do Programmers Discuss about Deep Learning Frameworks
Nächster Artikel What am I testing and where? Comparing testing procedures based on lightweight requirements annotations

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Fußnoten
1
ACT. Act state of the app enconomy 2018, 2018.
 
2
Findbugs™- find bugs in java programs. http://​findbugs.​sourceforge.​net/​.
 
3
PMD - an extensible cross-language static code analyzer. https://​pmd.​github.​io/​.
 
4
Android studio project site - Android Lint. http://​tools.​android.​com/​tips/​lint.
 
5
Findbugs™- find bugs in java programs. http://​findbugs.​sourceforge.​net/​.
 
6
Soot - A framework for analyzing and transforming Java and Android applications. https://​sable.​github.​io/​soot/​.
 
7
At the time of writing only FindBugs also provides a dedicated category for performance-related issues, but it is not specific to Android apps.
 
11
F-Droid - Free and Open Source Android App Repository. https://​f-droid.​org/​en/​.
 
12
At the time of writing this paper (June 2018) the search functionality on FDroid appears to be broken or not working. Furthermore, the https://​f-droid.​org/​forums/​search/​ endpoint that was used in the mining script does not exist anymore.
 
13
Wikipedia page on open-source Android apps, 2017.
 
15
This check is sound since the web page of an app in the Google Play store follows a fixed pattern, i.e., https://play.google.com/store/apps/details?id=[app package name].
 
16
Step 5 and Step 7 are redundant, we deliberately decided to keep both of them because during the execution of the dataset creation process we had to experiment with different heuristics in Step 7 and having it as a stand-alone step within the pipeline helped us in easily run it in isolation.
 
18
We take advantage of the fact that issues in Android Lint reports are tagged with a fixed set of categories (http://​tools.​android.​com/​tips/​lint-checks) like performance, correctness, accessibility, usability, etc.
 
22
In the remainder of the study we will refer to performance issues as P and to lines of code as LOC.
 
23
It is important to note that we performed the statistical analysis for RQ3 both with and without outliers and the conclusions did not change
 
27
Android lint checks.
 
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Metadaten
Titel
Characterizing the evolution of statically-detectable performance issues of Android apps
verfasst von
Teerath Das
Massimiliano Di Penta
Ivano Malavolta
Publikationsdatum
04.05.2020
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 4/2020
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-019-09798-3

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