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// For example, taint analysis datasets should provide the source and expected sink of a taint.
// In some cases, the datasets are provided with additional software for automatizing part of the analysis.
// Thus,
We review in this section the past existing datasets provided by the community and the papers related to static analysis tools reusability.
=== Application Datasets
Computing if an application contains a possible information flow is an example of a static analysis goal.
Some datasets have been built especially for evaluating tools that are computing information flows inside Android applications.
One of the first well known dataset is DroidBench, that was released with the tool Flowdroid@Arzt2014a.
Later, the dataset ICC-Bench was introduced with the tool Amandroid@weiAmandroidPreciseGeneral2014 to complement DroidBench by introducing applications using Inter-Component data flows.
These datasets contain carefully crafted applications containing flows that the tools should be able to detect.
These hand-crafted applications can also be used for testing purposes or to detect any regression when the software code evolves.
Contrary to real world applications, the behavior of these hand-crafted applications is known in advance, thus providing the ground truth that the tools try to compute.
However, these datasets are not representative of real-world applications@Pendlebury2018 and the obtained results can be misleading.
//, especially for performance or reliability evaluation.
Contrary to DroidBench and ICC-Bench, some approaches use real-world applications.
Bosu #etal@bosuCollusiveDataLeak2017 use DIALDroid to perform a threat analysis of Inter-Application communication and published DIALDroid-Bench, an associated dataset.
Similarly, Luo #etal released TaintBench@luoTaintBenchAutomaticRealworld2022 a real-world dataset and the associated recommendations to build such a dataset.
These datasets confirmed that some tools such as Amandroid@weiAmandroidPreciseGeneral2014 and Flowdroid@Arzt2014a are less efficient on real-world applications.
These datasets are useful for carefully spotting missing taint flows, but contain only a few dozen of applications.
// A larger number of applications would be more suitable for our goal, #ie evaluating the reusability of a variety of static analysis tools.
Pauck #etal@pauckAndroidTaintAnalysis2018 used those three datasets to compare Amandroid@weiAmandroidPreciseGeneral2014, DIAL-Droid@bosuCollusiveDataLeak2017, DidFail@klieberAndroidTaintFlow2014, DroidSafe@DBLPconfndssGordonKPGNR15, FlowDroid@Arzt2014a and IccTA@liIccTADetectingInterComponent2015 -- all these tools will be also compared in this paper.
To perform their comparison, they introduced the AQL (Android App Analysis Query Language) format.
AQL can be used as a common language to describe the computed taint flow as well as the expected result for the datasets.
It is interesting to notice that all the tested tools timed out at least once on real-world applications, and that Amandroid@weiAmandroidPreciseGeneral2014, DidFail@klieberAndroidTaintFlow2014, DroidSafe@DBLPconfndssGordonKPGNR15, IccTA@liIccTADetectingInterComponent2015 and ApkCombiner@liApkCombinerCombiningMultiple2015 (a tool used to combine applications) all failed to run on applications built for Android API 26.
These results suggest that a more thorough study of the link between application characteristics (#eg date, size) should be conducted.
Luo #etal@luoTaintBenchAutomaticRealworld2022 used the framework introduced by Pauck #etal to compare Amandroid@weiAmandroidPreciseGeneral2014 and Flowdroid@Arzt2014a on DroidBench and their own dataset TaintBench, composed of real-world android malware.
They found out that those tools have a low recall on real-world malware, and are thus over adapted to micro-datasets.
Unfortunately, because AQL is only focused on taint flows, we cannot use it to evaluate tools performing more generic analysis.
=== Static Analysis Tools Reusability
We review in this section the past existing contributions related to static analysis tools reusability.
Several papers have reviewed Android analysis tools produced by researchers.
Li #etal@Li2017 published a systematic literature review for Android static analysis before May 2015.
@ -49,6 +18,19 @@ In particular, they listed 27 approaches with an open-source implementation avai
Nevertheless, experiments to evaluate the reusability of the pointed out software were not performed.
We believe that the effort of reviewing the literature for making a comprehensive overview of available approaches should be pushed further: an existing published approach with a software that cannot be used for technical reasons endanger both the reproducibility and reusability of research.
As we saw in @sec:bg-datasets that the need for a ground truth to test analysis tools leads test datasets to often be handcrafted.
The few datasets composed of real-world application confirmed that some tools such as Amandroid@weiAmandroidPreciseGeneral2014 and Flowdroid@Arzt2014a are less efficient on real-world applications@bosuCollusiveDataLeak2017 @luoTaintBenchAutomaticRealworld2022.
Unfortunatly, those real-world applications datasets are rather small, and a larger number of applications would be more suitable for our goal, #ie evaluating the reusability of a variety of static analysis tools.
Pauck #etal@pauckAndroidTaintAnalysis2018 used DroidBench@@Arzt2014a, ICC-Bench@weiAmandroidPreciseGeneral2014 and DIALDroid-Bench@@bosuCollusiveDataLeak2017 to compare Amandroid@weiAmandroidPreciseGeneral2014, DIAL-Droid@bosuCollusiveDataLeak2017, DidFail@klieberAndroidTaintFlow2014, DroidSafe@DBLPconfndssGordonKPGNR15, FlowDroid@Arzt2014a and IccTA@liIccTADetectingInterComponent2015 -- all these tools will be also compared in this chapter.
To perform their comparison, they introduced the AQL (Android App Analysis Query Language) format.
AQL can be used as a common language to describe the computed taint flow as well as the expected result for the datasets.
It is interesting to notice that all the tested tools timed out at least once on real-world applications, and that Amandroid@weiAmandroidPreciseGeneral2014, DidFail@klieberAndroidTaintFlow2014, DroidSafe@DBLPconfndssGordonKPGNR15, IccTA@liIccTADetectingInterComponent2015 and ApkCombiner@liApkCombinerCombiningMultiple2015 (a tool used to combine applications) all failed to run on applications built for Android API 26.
These results suggest that a more thorough study of the link between application characteristics (#eg date, size) should be conducted.
Luo #etal@luoTaintBenchAutomaticRealworld2022 used the framework introduced by Pauck #etal to compare Amandroid@weiAmandroidPreciseGeneral2014 and Flowdroid@Arzt2014a on DroidBench and their own dataset TaintBench, composed of real-world android malware.
They found out that those tools have a low recall on real-world malware, and are thus over adapted to micro-datasets.
Unfortunately, because AQL is only focused on taint flows, we cannot use it to evaluate tools performing more generic analysis.
A first work about quantifying the reusability of static analysis tools was proposed by Reaves #etal@reaves_droid_2016.
Seven Android analysis tools (Amandroid@weiAmandroidPreciseGeneral2014, AppAudit@xiaEffectiveRealTimeAndroid2015, DroidSafe@DBLPconfndssGordonKPGNR15, Epicc@octeau2013effective, FlowDroid@Arzt2014a, MalloDroid@fahlWhyEveMallory2012 and TaintDroid@Enck2010) were selected to check if they were still readily usable.
For each tool, both the usability and results of the tool were evaluated by asking auditors to install and use it on DroidBench and 16 real world applications.
@ -56,7 +38,7 @@ The auditors reported that most of the tools require a significant amount of tim
Reaves #etal propose to solve these issues by distributing a Virtual Machine with a functional build of the tool in addition to the source code.
Regrettably, these Virtual Machines were not made available, preventing future researchers to take advantage of the work done by the auditors.
Reaves #etal also report that real world applications are more challenging to analyze, with tools having lower results, taking more time and memory to run, sometimes to the point of not being able to run the analysis.
We will confirm and expand this result in this paper with a larger dataset than only 16 real-world applications.
We will confirm and expand this result in this chapter with a larger dataset than only 16 real-world applications.
// Indeed, a more diverse dataset would assess the results and give more insight about the factors impacting the performances of the tools.
Finally, our approach is similar to the methodology employed by Mauthe #etal for decompilers@mauthe_large-scale_2021.