Reducing the cost of programming

Different programming languages have different capabilities. And not surprisingly, different programming languages have different costs. Over the years, we have found ways of reducing those costs.

Costs include infrastructure (disk space for compiler, memory) and programmer training (how to write programs, how to compile, how to debug). Notice that the load on the programmer can be divided into three: infrastructure (editor, compiler), housekeeping (declarations, memory allocation), and business logic (the code that gets stuff done).

Symbolic assembly code was better than machine code. In machine code, every instruction and memory location must be laid out by the programmer. With a symbolic assembler, the computer did that work.

COBOL and FORTRAN reduced cost by letting the programmer not worry about the machine architecture, register assignment, and call stack management.

BASIC (and time-sharing) made editing easy, eliminated compiling, and made running a program easy. Results were available immediately.

Today we are awash in programming languages. The big ones today (C, Java, Objective C, C++, BASIC, Python, PHP, Perl, and JavaScript -- according to Tiobe) are all good at different things. That is perhaps not a coincidence. People pick the language best suited to the task at hand.

Still, it would be nice to calculate the cost of the different languages. Or if numeric metrics are not possible, at least rank the languages. Yet even that is difficult.

One can easily state that C++ is more complex than C, and therefore conclude that programming in C++ is more expensive that C. Yet that's not quite true. Small programs in C are easier to write than equivalent programs in C++. Large programs are easier to write in C++, since the ability to encapsulate data and group functions into classes helps one organize the code. (Where 'small' and 'large' are left to the reader to define.)

Some languages are compiled and some that are interpreted, and one can argue that a separate step to compile is an expense. (It certainly seems like an expense when I am waiting for the compiler to finish.) Yet languages with compilers (C, C++, Java, C#, Objective-C) all have static typing, which means that the editor built into an IDE can provide information about variables and functions. When editing a program written in one of the interpreted languages, on the other hand, one does not have that help from the editor. The interpreted languages (Perl, Python, PHP, and JavaScript) have dynamic typing, which means that the type of a variable (or function) is not constant but can change as the program runs.

Switching from an "expensive" programming language (let's say C++) to a "reduced cost" programming language (perhaps Python) is not always possible. Programs written in C++ perform better. (On one project, the C++ program ran for several hours; the equivalent program in Perl ran for several days.) C and C++ let one have access to the underlying hardware, something that is not possible in Java or C# (at least not without some add-in trickery, usually involving... C++.)

The line between "cost of programming" and "best language" quickly blurs, and nailing down the costs for the different dimensions of programming (program design, speed of coding, speed of execution, ability to control hardware) get in our way.

In the end, I find that it is easy to rank languages in the order of my preference rather than in an unbiased scheme. And even my preferences are subject to change, given the nature of the project. (Is there existing code? What are other team members using? What performance constraints must we meet?)

Reducing the cost of programming is really about trade-offs. What capabilities do we desire, and what capabilities are we willing to cede? To switch from C++ to C# may mean faster development but slower performance. To switch from PHP to Java may mean better organization of code through classes but slower development. What is it that we really want?