Code Quality - Which Implementation is “Better”?

date:2007-01-03 11:39:50 category:Culture of Complexity

To begin with, we’re talking about the implementation’s source code. Here’s the situation: Ann says that Bob’s code is no good, and she’s provided an alternative implementation. How do we choose?

The first part of any scorecard is the SEI Quality Measures Taxonomy . It’s marked “Legacy”, but I haven’t seen anything as good or complete to replace it.

There are five top-level categories on which you can evaluate any implementation. Actual numeric scores for these are hard to come by, since they don’t all have units and measurement instruments defined. However, you can use subjective scores. More importantly, these topics focus the conversation around useful quality attributes.

Here are the five top-level categories:

• Need Satisfaction. In short, does it work? Does it solve the problem in the first place?
• Performance. Here is where the classical memory and time considerations are evaluated.
• Maintenance. How easy is it to fix?
• Adaptive. How easy is it to extend or port?
• Organizational. How expensive is it? Do we have the skills?

The SEI taxonomy has 16 subcategories, enough to cover all the bases. For me, this is the final word on quality, and I haven’t seen anything better than this.

Understandability.

Under “Maintenance”, there’s a subcategory of “Understandability”, which includes four lower-level metrics:

• Complexity
• Simplicity
• Structuredness
• Readability

This “Understanability” group is so important that we need to pull it out and evaluate it by itself. The SEI information directs us to a “Maintainability Index ” technique that combines a number of metrics. Detailed values are a pain to gather because they involve parsing the source code, something that isn’t very quick or easy to do.

On the other hand, an informal survey of the source can be done using the following techniques. These aren’t the official numbers, but they’re a way to have a useful conversation about which implementation is “better”. They provide a workable definition of “understandable” that – to an extent – is free from subjective complaints about someone else’s coding style.

• Halstead Length = total number of variable occurrences plus total number of method calls, operators, and (in Python) function calls. How long is the component, not in lines of code, but in things the program represents and things the program does?
• Halstead Volume = number of distinct variables plus distinct methods, operators and functions. Often a much smaller number than the length. This is “volume” in the sense of how many distinct things do you have to keep in your head to understand it; it’s the brain volume tied up in working on the program.
• McCabe Complexity = number of nested if , while and for statements. In the case of Python elif constructs, these count, also. Statements at the “top-level” of a method or function don’t count, only the nested ones. Yes, you can finesse this score to zero with a lot of small methods, that would be the whole point.
• Total source lines of code. If you want, you can segregate comments from non-comments. Python and Java programmers have three categories: the Javadoc (or docstring) comments, the “other” comments, and the remaining code. The Javadoc (or docstring) comments have to be taken as seriously as the code itself.

These can be combined into a single score, but that defeats the purpose of looking at the code and evaluating it based on the textual complexity (the Halstead metrics) and the structural complexity (the McCabe) metric.

The Evils of If-Statements.

I find that my own quick-and-dirty McCabe metric works well in evaluating complex. I count if -statements. In my experience, little good comes from if -statements. Each one needs to be scrutinized carefully and justified as essential.

Generally, every one of the cool design patterns in the Design Patterns: Elements of Reusable Object-Oriented Software book can be replaced with an amazingly poor cluster of rotten, unmanageable if -statements. In particular, State , Command and Strategy are often bollixed up into a horrifying morass of iffiness.

Here’s the conversation:

Customer : “Make it do ‘X’, not ‘Y’, in this situation.”

Me : “What does that mean? Why is this situation unique?”

Customer : “That doesn’t matter; there’s no underlying meaning. It’s just an if -statement.”

Everything has meaning. There’s no such thing as “just an if -statement”. Generally, the things we think of as “conditions” are not last-minute decisions in the middle of an algorithm. They are starting conditions that describe what objects we should be created in the first place. Most if -statements belong in some sort of Factory or Builder : they have deep and pervasive meaning.