Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS.
OpenHarmony, a newly established open-sourced mobile platform launched by the OpenAtom Foundation after receiving a donation of the open-source code from Huawei (more info can be find
here), has rarely been considered by our fellow researchers, although it should be the one requiring the most attention as OpenHarmony is a new platform that hasn't been fully validated compared to its counterparts.
To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony.
By learning from the Mobile Software Engineering community, as illustrated in the following picture, we find that static analysis, dynamic analysis, and machine learning are the most recurrently leveraged techniques for supporting the implementation of the various MSE tasks.
To this end, we start by building the following two fundamental frameworks, respectively for static analysis and dynamic analysis, for the community, aimed at promoting OpenHarmony Software Engineering research.
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ArkAnalyzer: The static analysis framework for OpenHarmony
[WWW]
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HapTest: The dynamic analysis framework for OpenHarmony
[WWW]
