#import "@local/template-thesis-matisse:0.0.1": etal
#import "X_var.typ": *

== Conclusion <sec:rasta-conclusion>

This paper has assessed the suggested results of the literature@luoTaintBenchAutomaticRealworld2022 @pauckAndroidTaintAnalysis2018 @reaves_droid_2016 about the reliability of static analysis tools for Android applications. 
With a dataset of #NBTOTALSTRING applications we established that #resultunusable of #nbtoolsselectedvariations tools are not reusable, when considering that a tool that has more than 50% of time a failure is unusable. 
In total, the analysis success rate of the tools that we could run for the entire dataset is #resultratio. 
The characteristics that have the most influence on the success rate is the bytecode size and min SDK version. Finally, we showed that malware APKs have a better finishing rate than goodware.

In future works, we plan to investigate deeper the reported errors of the tools in order to analyze the most common types of errors, in particular for Java based tools. 
We also plan to extend this work with a selection of more recent tools performing static analysis.

Following Reaves #etal recommendations@reaves_droid_2016, we publish the Docker and Singularity images we built to run our experiments alongside the Docker files. This will allow the research community to use directly the tools without the build and installation penalty.
