Pragmatic Software Metrics

Measuring is useful, but it doesn't do any thinking for the decision makers. It only provides data to help them ask-tne right questions, understand the context, and make objective decisions. Because of the highly dynamic nature of software projects, these measures must be available at any time, tailorable to various subsets of the evolving product (release, version, component, class), and maintained so that trends can be assessed (first and second derivatives with respect to time). This situation has been achieved in practice only in projects where the metrics were maintained on-line as an automated by-product of the development/integration environment. The basic characteristics of a good metric are as follows:

1. It is considered meaningful by the customer, manager, and performer. If any one of these stakeholders does not see the metric as meaningful, it will not be used. "The customer is always right" is a sales motto, not an engineering tenet. Customers come to software engineering providers because the providers are more expert than they are at developing and managing software. Customers will accept metrics that are demonstrated to be meaningful to the developer.

2. It demonstrates quantifiable correlation between process perturbations and business performance. The only real organizational goals and objectives are financial: cost reduction, revenue increase, and margin increase.

3. It is objective and unambiguously defined. Objectivity should translate into some form of numeric representation (such as numbers, percentages, ratios) as opposed to textual representations (such as excellent, good, fair, poor). Ambiguity is minimized through well-understood units of measurement (such as staff-month, SLOC, change, function point, class, scenario, requirement), which are surprisingly hard to define precisely in the software engineering world.

4. It displays trends. This is an important characteristic. Understanding the change in a metric's value with respect to time, subsequent projects, subsequent releases, and so forth is an extremely important perspective, especially for today's iterative development models. It is very rare that a given metric drives the appropriate action directly. More typically, a metric presents a perspective. It is up to the decision authority (manager, team, or other information processing entity) to interpret the metric and decide what action is necessary.

5. It is a natural by-product of the process. The metric does not introduce new artifacts or overhead activities; it is derived directly from the mainstream engineering and management workflows.

6. It is supported by automation. Experience has demonstrated that the most successful metrics are those that are collected and reported by automated tools, in part because software tools require rigorous definitions of the data they process.

When metrics expose a problem, it is important to get underneath all the symptoms and diagnose it. Metrics usually display effects; the causes require synthesis of multiple perspectives and reasoning. For example, reasoning is still required to interpret the following situations correctly:

• A low number of change requests to a software baseline may mean that the software is mature and error-free, or it may mean that the test team is on vacation.

• A software change order that has been open for a long time may mean that the problem was simple to diagnose and the solution required substantial rework, or it may mean that a problem was very time-consuming to diagnose and the solution required a simple change to a single line of code.

• A large increase in personnel in a given month may cause progress to increase proportionally if they are trained people who are productive from the outset. It may cause progress to decelerate if they are untrained new hires who demand extensive support from productive people to get up to speed.

Value judgments cannot be made by metrics; they must be left to smarter entities such as software project managers.

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