Chapter 13: Make continuous improvement a reality
13.3 Why should we approach improvements as experiments?
Some changes, especially those coming down from management, are enacted as rules. For instance, some companies put in place the rule “X new unit tests per programmer per iteration” before starting a more formal Agile transition. Another popular rule is, “A story’s not done until its owner has applied the code reviewer’s comments.” The team or management establishes these rules for a good reason, such as improving quality or reducing costs.
This approach usually has two shortcomings:
- The rules are pre-determined to be the right measure. No attempt is made to assess their effect on performance. And sometimes they cause an unnoticed reversal elsewhere.
- The rules are an imitation of others’ success. When you hear someone refer to a tactic or rule as a “best practice”, you know that the context for its success has been lost. No practice is universally best; all practices have contexts in which they are costlier or less effective than others.
A better approach is to consider each improvement an experiment, with all the implications of the scientific method. Formulate a hypothesis; test it out and collect data; analyze your data and draw a conclusion. Since a work environment is a complex adaptive system, other techniques such as variable isolation and control groups might well be a stretch. Objective measures are also not always possible, but having a hypothesis to prove or disprove, and data for analysis, will get you far. Just be aware that the Hawthorne Effect might create misleading, short-term improvements.
Several teams I’ve coached were theoretically interested in pairing, but quite resistant to it in practice. They became considerably more receptive once I suggested treating it as an experiment (I used my magic phrase, ”Let’s give it a try.“) We usually limited the experiment to two or three iterations. I helped the teams establish clear rules (what to pair on, how often to switch, etc.), collect objective measurements such as defect data and completed story points, and solicit subjective measures such as satisfaction and knowledge acquisition. Time-boxing the experiment assured the participants of having a way out if they didn’t like it.
Reframing a change as an experiment — and carrying it out as a bona fide experiment — is a great way to get buy-in. For stronger buy-in and participation in an experiment, it’s better to have the team agree to run it. If the team can reach consensus on an experiment, they’ll feel more curious about the hypothesis and more congruent about their participation than if they’re told, ”You are going to pair up for the next two iterations, and after we process the results, we’ll tell you whether to keep pairing or not.“
It’s important to realize that you’re not just reframing a change as an experiment. Agile teams and organizations are such complex systems that you don’t truly know whether the change would result in net improvement. For instance, many people consider code reviews a ”best practice“ for enforcing standards and finding omissions. In some teams who find them beneficial, code reviews also cause considerable context switching, delays in story completion, and costly overhead of managing the findings.
Beware the Law of Unintended Consequences. Whatever action you take, you might receive an unexpected benefit, cause an undesirable effect, or make the underlying problem worse. As you formulate your hypothesis and the experiment, consider what other consequences might occur. For instance, one Agility assessment I conducted revealed that senior developers used the code review mechanism to sneak in many changes and gold-plating tasks, thereby messing up estimates and bypassing the backlog prioritization mechanism. For a systematic exploration of possible unintended consequences, consider their five possible causes: ignorance, error, immediate interest, basic values, and self-defeating prophecy.
Lastly, each experiment has value on several dimensions. The obvious one is the added learning. Another one is team building: the team committed to a shared activity, performed it, and followed up on it. And there is the value of humility when an experiment’s results are nothing like the hypothesis.