Title
Automated Translation of Legacy Code for ATE
Abstract
When an Automated Testing Equipment (ATE) company designs a new system, the issue of backward compatibility is always a major concern, both for the company and its customers. If backward compatibility is maintained, the ATE application engineers face the difficult task of trying to support new features on an aging system. The alternative is to face the problem of converting old test programs to the new environment. Translation of legacy code involves an automatic translation tool, and some application effort applied to those problems the translatorcouldn't resolve. To minimize the amount of work required from the application engineers, the tool need to be semantically-aware; that is, the tool must contain domain-specific knowledge and use that knowledge when translating. The more knowledge a tool has at its disposal, the less code an application engineer is forced to translate by hand.Until recently, it has been difficult to perform automatic translation satisfactorily because it was not cost effective to write a translator that possessed such semantic understanding of the test programs. By making good use of Functional Programming techniques and tools, we were able to construct a cost-effective, semantically-aware translation tool in a fraction of the time needed by traditional methods. Based upon its performance during testing, we believe the toolto correctly translate the majority of test programs, thereby greatly easing the applications engineers' burden.
Year
Venue
Keywords
2001
ITC
automated translation,application engineer,ate application engineer,legacy code,automatic translation tool,automatic translation,new environment,test program,semantically-aware translation tool,domain-specific knowledge,application effort,new feature,automated test equipment
Field
DocType
ISSN
Software engineering,Functional programming,Computer science,Electronic engineering,Legacy code,Automatic translation,Backward compatibility
Conference
1089-3539
ISBN
Citations 
PageRank 
0-7803-7171-2
1
0.35
References 
Authors
2
5
Name
Order
Citations
PageRank
Andrew Moran110.35
Jim Teisher2133.13
Andrew Gill310.35
Emir Pasalic419255.42
John Veneruso510.35