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A framework that creates Android apps from textual content descriptions


Text2App: A framework that creates Android apps from text descriptions
Text2App creates a practical Android software from specification offered in English. Credit score: Hasan et al.

Researchers at Bangladesh College of Engineering and Know-how (BUET) and College of California- Los Angeles (UCLA) just lately developed a framework that may create Android purposes from textual content descriptions. This new app was introduced in a paper pre-published on arXiv.

“Our analysis group on the Division of CSE, BUET works on bridging the hole between human language and programming language to make laptop programming extra accessible,” Masum Hasan, one of many researchers who carried out the examine, informed TechXplore. “We’ve got beforehand labored on pure language-based program restore, language-based code search, code doc era, and so forth. In continuation, we puzzled if totally functioning software program could possibly be constructed from pure language specification.”

Most current fashions for creating software program primarily based on textual content descriptions are primarily based on end-to-end neural machine translation (NMT) fashions, much like the one underpinning Google Translate. These fashions make the most of NMT frameworks to translate human language into supply code.

Whereas some NMT-based strategies for textual content to app translation have achieved promising outcomes, most of them are unable to generate giant packages with a whole lot of strains of code. To beat this limitation, Hasan and his colleagues created a brand new formal language that captures the complexity of app supply code, representing it in a extremely compact kind.

“We designed an intermediate formal language that may characterize a consumer specified app in a fraction of the tokens—as a lot as 20 occasions much less,” Hasan stated. “We then developed a compiler that may convert the compact illustration into precise supply code and educated a neural machine translation mannequin to translate pure language to this intermediate format, which may then be compiled into an app.”

Basically, the researchers educated a translation mannequin to generate representations within the intermediate language they created, relatively than creating uncooked supply code, as most current textual content to app strategies would. By reworking textual content descriptions into this formal language, their framework allowed them to create totally fledged cellular purposes, which proved unfeasible utilizing beforehand proposed strategies.

“Producing lengthy sequences is a recognized limitation of present sequence era fashions,” Hasan defined. “Supply code is often for much longer than pure language and the present greatest fashions nonetheless wrestle to realize 20% accuracy in producing brief code snippets. This makes it impractical to create giant software program, comparable to cellular purposes consisting of a whole lot of strains of codes, with present sequence era networks.”

The framework created by Hasan and his colleagues, dubbed Text2App, can create Android purposes utilizing directions in English. To make use of the system, customers merely want to explain a cellular software with a particular scope and the framework routinely creates it for them. A video demo of Text2App is now accessible on GitHub, at: https://text2app.github.io/

Hasan stated. “Think about you need to memorize your grandmother’s particular recipe for pasta. As a substitute of remembering the whole recipe step-by-step, you’ll be able to solely be taught the steps which are distinctive, and lookup the remainder of the steps from a cookbook. This can considerably cut back the quantity of effort you would need to spend. Equally, our intermediate app illustration solely captures the precise necessities offered by the consumer by way of the pure language instruction and the compiler fills in the remainder.”

The current work by Hasan and his colleagues might permit researchers to re-think program and app era, by introducing a brand new means by way of which packages will be summarized or represented that’s simpler for AI know-how to grasp. Notably, the AI fashions educated by the researchers exhibited a far larger capability than different strategies to transform pure language into apps.

“If we design programming languages or frameworks with AI in thoughts, our present AI applied sciences can carry out remarkably higher in code era from human language,” Hasan stated. “Moreover, programming languages are made to be understood by compilers, and never fully by AIs. We exhibit that counting on AI fashions alone for advanced duties like code era is just not sufficient, and extra help from parts like compilers can assist an important deal.”

Probably the most notable achievement of the examine carried out by this workforce of researchers is the event of the intermediate formal language that allows the conversion of textual content into an app, in addition to a compiler that in the end creates the app. Thus far, Hasan and his colleagues explored a mere fraction of its capabilities, however they’re now additionally asking the open-source neighborhood to contribute to its additional analysis and growth.

“With extra growth efforts, Text2App can develop into a full-fledged pure language- primarily based app growth platform,” Hasan stated. “In our work, we additionally present that it’s potential to create purposes from surprisingly advanced directions with our system with the assistance of a big language mannequin, GPT-3. Nonetheless, extra ideas on this course are essential to make it totally dependable.”


A synthetic neural community to amass grounded representations of robotic actions and language


Extra data:
Text2App: a framework for creating android apps from textual content descriptions. arXiv:2104.08301 [cs.CL]. arxiv.org/abs/2104.08301

Review4Repair: Code assessment aided automated program repairing. arXiv:2010.01544 [cs.SE]. arxiv.org/abs/2010.01544

Language fashions are few-shot learners. arXiv:2005.14165 [cs.CL]. arxiv.org/abs/2005.14165

CoDesc: A big code-description parallel dataset. arXiv:2105.14220 [cs.CL]. arxiv.org/abs/2105.14220

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