Synthesis of mobile applications using AgileUML

Lano, Kevin; Alwakeel, Lyan; Kolahdouz-Rahimi, Shekoufeh

doi:10.1145/3452383.3452409

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Developing mobile apps requires significant effort because of the complexity of self-management mHealth apps. Therefore, we have started the development of a framework that accelerates and facilitates the development of mHealth apps [ 82 , 83 ]. The framework semiautomatically generates Android and iOS mobile apps and will be enhanced with frequent characteristics that resulted from this review, such as tab structure, predefined components of ML algorithms, and local and external services.…”

Section: Discussionmentioning

confidence: 99%

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

Alwakeel¹,

Lano²

2022

JMIR Hum Factors

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Background Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective. Objective This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps. Methods This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub. Results In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges. Conclusions Self-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

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Section: Discussionmentioning

confidence: 99%

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

Alwakeel¹,

Lano²

2022

JMIR Hum Factors

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“…CST L has been applied to the generation of Swift 5 and Java 8 code, to support mobile app synthesis [20]. It has also been used to generate Go code from UML, and applied to natural language processing [22] and reverse-engineering tasks [18].…”

Section: Lhs |--> Rhs Conditionmentioning

confidence: 99%

Code Generation by Example Using Symbolic Machine Learning

Lano

Xue

2023

SN COMPUT. SCI.

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Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs. We evaluate the approach on several code generation tasks, and compare the approach to other code generator construction approaches. The results show that the approach can effectively automate the synthesis of code generators from examples, with relatively small manual effort required compared to existing code generation construction approaches. We also identified that it can be adapted to learn software abstraction and translation algorithms. The paper demonstrates that a symbolic machine learning approach can be applied to assist in the development of code generators and other tools manipulating software syntax trees.

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“…Source: Authors could be revised (stayDuration could be moved to Inpatient, for example). From this model Java code can be generated for a prototype app implementation (Lano et al, 2021), together with a test suite and test harness (Figure 8).…”

Section: Integration Of Automated Requirements Engineering Into An Ag...mentioning

confidence: 99%

“…The toolset can be utilised to synthesise code in several 3GLs, including C, Java, Python and Swift. Mobile apps for the Android and iOS platforms can also be generated (Lano et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Lano

Tehrani²,

Umar³

et al. 2022

Future of Business and Finance

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Artificial intelligence (AI) techniques such as machine learning (ML) have wide application in medical informatics systems. In this chapter we employ AI techniques to assist in deriving software specifications of e-Health and m-Health systems from informal requirements statements. We use natural language processing (NLP), optical character recognition (OCR) and machine learning to identify required data and behaviour elements of systems from textual and graphical requirements documents. Heuristic rules are used to extract formal specification models of the systems from these documents. The extracted specifications can then be used as the starting point for automated software production using model-driven engineering (MDE). We illustrate the process using an example of a stroke recovery assistant app, and evaluate the techniques on several representative systems.

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Synthesis of mobile applications using AgileUML

Cited by 3 publications

References 19 publications

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

Code Generation by Example Using Symbolic Machine Learning

Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

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