Automatic Generation of Programs

Abstract

Automatic generation of program is definitely an alluring problem. Over the years many approaches emerged, which try to smooth away parts of programmers’ work. One approach already widely used today is colloquially known as code generation (or code generators). This approach includes many methods and tools, therefore many different terms are used to describe this concept. The very basic tools are included in various available Integrated Development Environments (IDE). These include templates, automatic code completion, macros and other tools. On a higher level, code generation is performed by tools, which create program source code from metadata or data. Again, there are thousands of such tools available both commercial and open source. Generally available are programs for generating source code from relational or object database schema, object or class diagrams, test cases, XML schema, XSD schema, design patterns or various formalized descriptions of the problem domain. These tools mainly focus on the generation of a template or skeleton for an application or application module, which is then filled with actual algorithms by a programmer. The great advantage of such tools is that they lower the amount of tedious, repetitive and boring (thus error-prone) work. Commonly the output is some form of data access layer (or data access objects) or object relational mapping (ORM) or some kind of skeleton for an application for example interface for creating, reading, updating and deleting objects in database (CRUD operations). Further, this approach leads to generative programming domain, which includes concepts such as aspect-oriented programming (Gunter & Mitchell, 1994), generic programming, meta-programming etc. (Czarnecki & Eisenecker, 2000). These concepts are now available for general use – for example the AspectJ extension to Java programming language is considered stable since at least 2003 (Ladad, 2009). However, they are not still mainstream form of programming according to TIOBE Index (TIOBE, 2010). A completely different approach to the problem is an actual generation of algorithms of the program. This is a more complex then code generation as described above, since it involves actual creation of algorithms and procedures. This requires either extremely complex tools or artificial intelligence. The former can be probably represented by two most successful (albeit completely different) projects – Lyee project (Poli, 2002) and Specware project (Smith, 1999). Unfortunately, the Lyee project was terminated in 2004 and the latest version of Specware is from 2007. As mentioned above, another option is to leverage artificial intelligence methods (particularly evolutionary algorithms) and use them to create code evolution. We use the term