The Seven Core Metrics

Many different metrics may be of value in managing a modern process. I have settled on seven core metrics that should be used on all software projects. Three are management indicators and four are quality indicators.

Management Indicators

• Work and progress (work performed over time)

• Budgeted cost and expenditures (cost incurred over time)

• Staffing and team dynamics (personnel changes over time)

Quality Indicators

• Change traffic and stability (change traffic over time)

• Breakage and modularity (average breakage per change over time)

• Rework and adaptability (average rework per change over time)

• Mean time between failures (MTBF) and maturity (defect rate over time)

Table 13-1 describes the core software metrics. Each metric has two dimensions: a static value used as an objective, and the dynamic trend used to manage the achievement of that objective. While metrics values provide one dimension of insight, metrics trends provide a,more important perspective for managing the process. Metrics trends with respect to time provide insight into how the process and product are evolving. Iterative development is about managing change, and measuring change is the most important aspect of the metrics program. Absolute values of productivity and quality improvement are secondary issues until the fundamental goal of management has been achieved: predictable cost and schedule performance for a given level of quality.

Table 13-1. Overview of the seven core metrics




Work and progress

Iteration planning, plan vs. actuals, management indicator

SLOC, function points, object points, scenarios, test cases, SCOs

Budgeted cost and expenditures

Financial insight, plan vs. actuals, management indicator

Cost per month, full-time staff per month, percentage of budget expended

Staffing and team dynamics

Resource plan vs. actuals, hiring rate, attrition rate

People per month added, people per month leaving

Change traffic and stability

Iteration planning, management indicator of schedule convergence

SCOs opened vs. SCOs closed, by type (0,1,2,3,4), by release/component/ subsystem

Breakage and modularity

Convergence, software scrap, quality indicator

Reworked SLOC per change, by type (0,1,2,3,4), by release/component/subsystem

Rework and adaptability

Convergence, software rework, quality indicator

Average hours per change, by type (0,1,2,3,4), by release/component/ subsystem

MTBF and maturity

Test coverage/adequacy, robustness for use, quality indicator

Failure counts, test hours until failure, by release/component/ subsystem

Appendix C provides a brief derivation and detailed description of these metrics. They have been proven in practice on projects using iterative development. The case study in Appendix D presents a very detailed description of how such metrics can work on a real project.

The seven core metrics can be used in numerous ways to help manage projects and organizations. In an iterative development project or an organization structured around a software line of business, the historical values of previous iterations and projects provide precedent data for planning subsequent iterations and projects. Consequently, once metrics collection is ingrained, a project or organization can improve its ability to predict the cost, schedule, or quality performance of future work activities.

The seven core metrics are based on common sense and field experience with both successful and unsuccessful metrics programs. Their attributes include the following:

• They are simple, objective, easy to collect, easy to interpret, and hard to misinterpret.

• Collection can be automated and nonintrusive.

• They provide for consistent assessments throughout the life cycle and are derived from the evolving product baselines rather than from a subjective assessment.

• They are useful to both management and engineering personnel for communicating progress and quality in a consistent format.

• Their fidelity improves across the life cycle.

The last attribute is important and deserves further discussion. Metrics applied to the engineering stage (dominated by intellectual freedom and risk resolution) will be far less accurate than those applied to the production stage (dominated by implementation activities and change management). Therefore, the prescribed metrics are tailored to the production stage, when the cost risk is high and management value is leveraged. Metrics activity during the engineering stage is geared mostly toward establishing initial baselines and expectations in the production stage plan.

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