6th Sense Analytics

Blame vs. Praise — Simply a matter of attitude

Subversion ( SVN ), the popular source-code control system, has a command to see the changes to a file by user. It shows a file annotated line-by-line with the user responsible for authoring it. This command has two names: blame and praise. Whenever I jokingly suggest using "blame", developers will often respond, "you mean praise?"

Scenario 1
A blocking bug is found in a piece of code very late in a release cycle. The team has been working very hard for the previous 6 months and the product is due for release in days. The team ( and manager ) immediately want to determine why a blocking bug could be introduced so late in a release. svn blame.

Scenario 2
A team is performing a code review of an important module. Many members of the team have worked on the module, but one particular piece is very well-documented and well-written code. There is a test accompanying code that verifies its correctness. The team ( and manager ) wants to know who was responsible for this masterpiece. svn praise.

Same command different attitude

The command is the same. The information is the same. The only difference is the attitude employed by the folks looking at the information.

The same concept applies to software metrics. Sure it's possible to abuse metrics. We all can think of of ways to twist data for a negative purpose. We're all familiar with the classic cliche of "lies, damn lies, and statistics." The fact is that use of metrics has nothing to do with the metrics themselves -- it's simply a matter of the attitude of the individuals interpreting it.

"With great power comes great responsibility.: Spiderman

While the aforementioned quote is attributed to a cartoon character ( also FDR ), it cannot be more appropriate. Information coming from tools like svn praise and software metrics is empowering. It is the responsibility of the information holder to correctly use and interpret the information. If used incorrectly or rashly, it can undo the intent and worse -- cause folks to disdain the information itself.

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