Metric or Treat

A friend lent me two books on metrics. One was so thumpingly wrong I could not finish it. One was so joyfully right I had to read it end-to-end, despite the author's recommendation not to.

Dave Nicolette will be pleased to know that his book, Software Development Metrics, was the second of the two. Why so right? Because immediately I felt like I was in the hands of a pragmatist, a practitioner, and a personable guide. By page 8 he's chattily differentiated measures and metrics, identified a bunch of dimensions for projects, and then categorised metrics too. Along the way, he drops this piece of wisdom (p. 5):

The sort of development process you're using will influence your choice of metrics. Some metrics depend on the work being done in a certain way. A common problem is that people believe they're using a given process, when in fact they're working according to a conflicting set of assumptions. If you apply metrics that depend on the process being done correctly you won't obtain information that can help you steer the work or measure the results of process-improvement efforts. You have to measure what's really happening, regardless of the buzzwords people use to describe it.

For Nicolette, metrics face forwards (towards an emerging solution) or backwards (towards a predefined plan), are trailing indicators (give feedback on work done) or leading indicators (forecast future work), can have any of three functions (prediction, diagnosis, motivation), and can be used to steer work in progress or process improvements.

He provides axes on which to plot projects, acknowledging that the real world isn't quite as neat as the breakdown might suggest:

process model: linear (proceeds through gated phases), iterative (repeated refinement of the solution), time-boxed (iterative in increments), continuous flow (controls work in progress).
delivery mode: project (with a start and end date and some goal to achieve before delivery) or ongoing (repeated, frequent, incremental delivery).
approach: traditional (up-front planning followed by implementation) or adaptive (evolving plans based on implementation so far).

A set of metrics for both steering and process improvement are presented with Top Trump-style summaries to permit quick reference and comparison. (This is the point at which he advises not reading from start to finish.) These details include factors that are crucial for the success of the metric, and it's striking that they tend to require someone to make some effort to generate and/or record data. Good data.

There is no such thing as a free lunch.

The book has a table describing the applicability of metrics to project types but I found myself wanting some kind of visualisation of it, I think so that I could look for similarities and differences across the traditional and adaptive approaches:

I was interested by the similarities, although there are subtleties and caveats about usage in particular contexts that it's important to take note of. Other commentary on each of the metrics includes warnings about common anti-patterns and examples based on data provided in an accompanying spreadsheet.

The wisdom comes at regular intervals. For example when management wants a particular metric regardless of its validity (p. 42): "If you're required to report progress in this way then do so; just don't assume that you can use the numbers to make critical decisions about your work." There are other, more worthwhile, hills to die on.

If you (or your superiors) find yourselves tempted to attribute magic to the numbers, heed this warning: "metrics won't directly tell you the root causes of a problem; they'll only indicate when reality diverges from expectations or exceeds limits you've defined as 'normal.'" (p.108) Perhaps it's your expectation or your view of normal that needs refinement.

Finally, a motivation to try to find metrics that work for your stakeholders to help them work for you: "One of the key benefits of tracking planning predictability is that it can enhance stakeholder satisfaction ... When stakeholders know they can count on receiving more or less what they're told to expect, they feel confident in the delivery organization and offer greater autonomy and trust to development teams." (p.131)

As you might expect for a book that's designed to be dipped into the content can be repetitive across metrics which serve similar purposes. Despite this, and chapters which talk about using and reporting metrics in practice (with warnings), the book still comes in at a tight 160 pages and is an extremely easy read. A real treat.
Image: Manning

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