Model-Based Scaffolding Code Generation for Cross-Platform Applications

Inayatullah, Mohammad; Azam, Farooque; Anwar, Muhammad Waseem

doi:10.1109/iemcon.2019.8936289

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Generative models as scaffolds. The cases of P9 and P23 suggest that the support provided by the generative code model functioned as a kind of scaffold [43,55,96] that enabled them to complete a task that was otherwise too difficult or daunting (RQ3). Both of these participants were strong software engineers but their Python language skills were not at a level at which they could accomplish the translation task on their own (despite the fact that they met our recruitment criteria).…”

Section: 13mentioning

confidence: 99%

Better Together? An Evaluation of AI-Supported Code Translation

Weisz¹,

Müller²,

Ross³

et al. 2022

27th International Conference on Intelligent User Interfaces

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Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Software and its engineering → Designing software; • Computing methodologies → Generative and developmental approaches.

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Section: 13mentioning

confidence: 99%

Better Together? An Evaluation of AI-Supported Code Translation

Weisz¹,

Müller²,

Ross³

et al. 2022

27th International Conference on Intelligent User Interfaces

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“…In the work of Inayatullah et al [13], an MDA-based framework (Model-Driven Architecture), provides UML modeling to express the domain concepts, generating hybrid applications and RESTful Services in Asp.Net. AP-PIAN 10 and MENDIX 11 are commercial frameworks that support mobile application development and data interoperability between applications.…”

Section: Related Workmentioning

confidence: 99%

“…Interfaces are modeled visually and business rules and behavior are defined at a high abstraction level through custom tool editors, the result is executable code generated. Following the business line, WEBRATIO 13 [1] is a model-driven development platform based on Interaction Flow Modeling Language 14 and with a high abstraction level. WEBRATIO uses the Cordova 15 API as a base and considers the same platforms as MENDIX.…”

Section: Related Workmentioning

confidence: 99%

A holistic approach for cross-platform software development

Blanco,

Lucrédio

2021

Preprint

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Cross-platform development solutions can help to make software available on different devices and platforms. But these are normally restricted to preconfigured platforms and consider that each individual solution is equal or similar to each other. As a result, developers have to resort to native development and build individual solutions, one for each device/platform, that cooperate to deliver the desired global functionality. This article presents an approach that takes advantage of existing solutions and have support for extending and including new platforms, and distributing functionality across devices. The approach is based on a general-purpose language that raises the abstraction level in order to keep the software free from platform details. Automatic transformations produce executable code that can be properly divided and deployed separately into different platforms. The proposed approach was evaluated in four ways. In the first evaluation, an existing cross-platform system was recreated using the approach. The second and third evaluations was conducted with expert and novice developers, who tested the approach in practice. The fourth evaluation introduced support for cross-platform testing. Results have brought evidence supporting the following main contributions: use of a single environment, the ability to reuse similar concepts between platforms and the potential to reduce costs.

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