I made a mistake, please don’t shoot me

The major difference between commercial/academic written software is the handling of user mistakes, or to be more exact what is considered to be a user mistake. In the commercial world the emphasis is on keeping the customer happy, which translates into trying hard to gracefully handle any ‘mistake’ the user makes. Academic software is generally written to solve a research problem and is often very unforgiving of users failing to keep to the undocumented straight and narrow; given the context this unforgiving behavior is understandable, but sometimes such software is released to an unsuspecting world.

The R archive of contributed packages, CRAN, is a good example of the academic approach to writing software. I am an active user of many packages in this archive and its contributors have my heart-felt thanks. But on a regular basis I make a mistake when calling a function in one of these packages, get shot in the foot and am not best pleased.

What makes the situation worse is that my mistakes are often so trivial and easy to fix (by both me or the package authors). My most common ‘mistake’ is passing an argument whose type is not handled by the function, e.g., passing a data-frame to diag (why to I have to convert the argument using as.matrix, when diag could spot my mistake and do the conversion for me instead of returning some horrible mess).

Commercial software can also be unforgiving of user mistakes; in fact early versions of a lot of commercial software is just as unfriendly as academic software. The difference is that the commercial managers will make it their business to ensure that developers fix the code to make it user friendly. Competition ensures that those who don’t listen to their users go out of business.

Updating code to gracefully handle user mistakes is often a chore and many developers hate having to do it, managers are needed to prod developers into doing the work. The only purpose for more than half of the code in a commercial product may be to handle user mistakes and the percentage can approach 90%.

A lot of Open Source software has significant commercial backing, e.g., Linux, Apache, Firefox and gcc/llvm, which means it is somebodies job to make sure customer complaints are addressed.

What the R development team needs is more commercial backing (it appears to have very little, but I may be wrong). Then somebody can be hired to go through the popular packages to make then mistake friendly, feed the changes back to the original author and generally educate package developers about bullet proofing their code.