Videogames and software used by people across international markets may run better and more accurately in the future thanks to research done at North Carolina State University.

Working with researchers at Peking University in China, the NCSU team created a tool that translates embedded code faster and more easily. The tool could help users overcome challenges to selling products in different markets.

"This is a significant advance because it saves programmers from hunting through tens of thousands of lines of code," said , an assistant professor of computer science at NCSU. "Productivity goes up because finding the ‘need-to-translate’ strings can be done more quickly. The quality also goes up, because there is less chance that a programmer will make a mistake and overlook relevant code."

The National Science Foundation and the U.S. Army research Office provided funding support for the research.

More information about the tool will be offered at the International Conference on Software Engineering in May.

An abstract of the researchers’ paper spells out some information:

“Modern software applications require internationalization to be distributed to different regions of the world. In various situations, many software applications are not internationalized at early stages of development. To internationalize such an existing application, developers need to externalize some hard-coded constant strings to resource files, so that translators can easily translate the application into a local language without modifying its source code. Since not all the constant strings require externalization, locating those need-to-translate constant strings is a necessary task that developers must complete for internationalization. In this paper, we present an approach to automatically locating need-to-translate constant strings. Our approach first collects a list of API methods related to the Graphical User Interface (GUI), and then searches for need-to-translate strings from the invocations of these API methods based on string-taint analysis. We evaluated our approach on four real-world open source applications: RText, Risk, ArtOfIllusion, and Megamek. The results show that our approach effectively locates most of the need-to-translate constant strings in all the four applications.”