Authors:
(1) LI LI, Beihang University, China;
(2) XIANG GAO, Beihang University, China;
(3) HAILONG SUN, Beihang University, China;
(4) CHUNMING HU, Beihang University, China;
(5) XIAOYU SUN, The Australian National University, Australia;
(6) HAOYU WANG, Huazhong University of Science and Technology, China;
(7) HAIPENG CAI, Washington State University, Pullman, USA;
(8) TING SU, East China Normal University, China;
(9) XIAPU LUO, The Hong Kong Polytechnic University, China;
(10) TEGAWENDÉ F. BISSYANDÉ, University of Luxembourg, Luxembourg;
(11) JACQUES KLEIN, University of Luxembourg, Luxembourg;
(12) JOHN GRUNDY, Monash University, Australia;
(13) TAO XIE, Peking University, China;
(14) HAIBO CHEN, Shanghai Jiao Tong University, China;
(15) HUAIMIN WANG, National University of Defense Technology, China.
Table of Links
The State Of OpenHarmony Ecosystem
Overview Of Mobile Software Engineering
7 RELATED WORK
OpenHarmony software engineering is in its early stage and there are only limited works contributed to this field. Indeed, as highlighted in Section 3.4, there are only 8 papers presented on this aspect. In this section, we will not discuss these OpenHarmony-related works anymore. Instead, we take this opportunity to highlight related works that provide a research roadmap or position statement for guiding a new research field, or a survey including literature reviews for summarizing a mature research direction. We now highlight the representative ones.
Research Roadmap. One of the most representative research roadmap reports is the one presented by Cheng et al. [22] who have proposed to conduct software engineering research for self-adaptive systems. After thorough discussions among the authors at a Dagstuhl seminar on Software Engineering for Self-Adaptive Systems, the authors have identified four views that are deemed essential to the software engineering of self-adaptive systems. For each view, the authors then summarize the state-of-the-art and highlight the challenges that should be addressed in order to achieve the final goal, i.e., the software is able to automatically cope with the complexity of today’s software-intensive systems. The authors have released another version (called the second research roadmap) five years later after the success of the first version. The goal of this second roadmap paper [25] remains the same, i.e., to summarize the state-of-the-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. Other representative research roadmap papers include the one proposed by France et al. [34] who advocate model-driven development of complex software as well as the one proposed by Papazoglou et al. [85] who advocate service-oriented computing as a new computing paradigm for supporting the development of rapid, low-cost and easy composition of distributed applications. Both of these works have summarized the state-of-the-art and challenges faced by ongoing research activities. More recently, McDermott et al. [79] present a research roadmap about Artificial Intelligence for Software Engineering (AI4SE) and Software Engineering for Artificial Intelligence (SE4AI), presenting key aspects aiming at enabling traditional systems engineering practice automation (AI4SE), and encourage new systems engineering practices supporting a new wave of automated, adaptive, and learning systems (SE4AI).
Literature Review. A literature review involves surveying scholarly sources (mainly research publications) on a specific topic, aiming to provide an overview of the state-of-the-art that is further backed up with a critical evaluation of the material. Except for providing a reflection on the past, it also gives a clear picture of the state of knowledge on the subject that is helpful for guiding future research directions. Because of the aforementioned benefits, in this work, we have resorted to surveying the literature review papers (instead of the majority of primary publications) presented in the field of mobile software engineering. Actually, conducting a survey of surveys is not new to the community. Our fellow researchers have explored this type of study in various domains when the number of primary publications kept increasing until it became difficult to follow the growing body of literature papers in the field. For example, AI-Zewairi et al. [3] have conducted a survey of surveys related to agile software development methodologies, which have gained rigorous attention in the software engineering community with an excessive number of research studies published. As another example, McNabb et al. [80] have presented to the community a survey of surveys about information visualization, which has also become extremely popular and the number of publications has become increasingly difficult to follow. Other representative works include the one proposed by Giraldo et al. [41] who have proposed a survey of surveys on the topic of security and privacy in Cyber-physical systems as well as the one proposed by Chatzimparmpas et al. [20] who have conducted a survey of surveys on the use of visualization for interpreting machine learning models.
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