Software Engineering-Process Metrics and Software Process Improvement

The only rational way to improve any process is to measure specific attributes of the process, develop a set of meaningful metrics based on these attributes, and then use the metrics to provide indicators that will lead to a strategy for improvement. But before we discuss software metrics and their impact on software process improvement, it is important to note that process is only one of a number of “controllable factors in improving software quality and organizational performance.”

Process sits at the center of a triangle connecting three factors that have a profound influence on software quality and organizational performance. The skill and motivation of people has been shown  to be the single most influential factor in quality and performance. The complexity of the product can have a substantial impact on quality and team performance. The technology (i.e., the software engineering methods) that populate the process also has an impact. In addition, the process triangle exists within a circle of environmental conditions that include the development environment (e.g., CASE tools), business conditions (e.g., deadlines, business rules), and customer characteristics (e.g., ease of communication).

We measure the efficacy of a software process indirectly. That is, we derive a set of metrics based on the outcomes that can be derived from the process. Outcomes include measures of errors uncovered before release of the software, defects delivered to and reported by end-users, work products delivered (productivity), human effort expended, calendar time expended, schedule conformance, and other measures. We also derive process metrics by measuring the characteristics of specific software engineering tasks.

Grady argues that there are “private and public” uses for different types of process data. Because it is natural that individual software engineers might be sensitive to the use of metrics collected on an individual basis, these data should be private to the individual and serve as an indicator for the individual only. Examples of private metrics include defect rates (by individual), defect rates (by module), and errors found during development. 

The “private process data” philosophy conforms well with the personal software process approach proposed by Humphrey Humphrey describes the approach in the following manner:

The personal software process (PSP) is a structured set of process descriptions, measurements, and methods that can help engineers to improve their personal performance. It provides the forms, scripts, and standards that help them estimate and plan their work. It shows them how to define processes and how to measure their quality and productivity. A fundamental PSP principle is that everyone is different and that a method that is effective for one engineer may not be suitable for another. The PSP thus helps engineers to measure and track their own work so they can find the methods that are best for them.

Humphrey recognizes that software process improvement can and should begin at the individual level. Private process data can serve as an important driver as the individual software engineer works to improve. Some process metrics are private to the software project team but public to all team members. Examples include defects reported for major software functions (that have been developed by a number of practitioners), errors found during formal technical reviews, and lines of code or function points per module and function. These data are reviewed by the team to uncover indicators that can improve team performance.

Public metrics generally assimilate information that originally was private to individuals and teams. Project level defect rates (absolutely not attributed to an individual), effort, calendar times, and related data are collected and evaluated in an attempt to uncover indicators that can improve organizational process performance. Software process metrics can provide significant benefit as an organization works to improve its overall level of process maturity. However, like all metrics, these can be misused, creating more problems than they solve. Grady suggests a “software metrics etiquette” that is appropriate for both managers and practitioners as they institute a process metrics program:

Use common sense and organizational sensitivity when interpreting metrics data.
Provide regular feedback to the individuals and teams who collect measures and metrics.
Don’t use metrics to appraise individuals.
Work with practitioners and teams to set clear goals and metrics that will be used to achieve them.
Never use metrics to threaten individuals or teams.
Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement.
Don’t obsess on a single metric to the exclusion of other important metrics.

As an organization becomes more comfortable with the collection and use of process metrics, the derivation of simple indicators gives way to a more rigorous approach called statistical software process improvement (SSPI). In essence, SSPI uses software failure analysis to collect information about all errors and defects encountered as an application, system, or product is developed and used. Failure analysis works in the following manner:

1. All errors and defects are categorized by origin .
2. The cost to correct each error and defect is recorded.
3. The number of errors and defects in each category is counted and ranked in descending order.
4. The overall cost of errors and defects in each category is computed.
5. Resultant data are analyzed to uncover the categories that result in highest cost to the organization.
6. Plans are developed to modify the process with the intent of eliminating (or reducing the frequency of) the class of errors and defects that is most costly.
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