Software process and project metrics are quantitative measures that enable software engineers to gain insight into the efficiency of the software process and the projects conducted using the process framework. In software project management, we are primarily concerned with productivity and quality metrics. There are four reasons for measuring software processes, products, and resources (to characterize, to evaluate, to predict, and to improve).
Measures and Metrics
Measure - provides a quantitative indication of the size of some product or process attribute
Measurement - is the act of obtaining a measure
Metric - is a quantitative measure of the degree to which a system, component, or process possesses a given attribute
Process Indicators
Metrics should be collected so that process and product indicators can be ascertained
Process indicators enable software project managers to: assess project status, track potential risks, detect problem area early, adjust workflow or tasks, and evaluate team ability to control product quality
Process Metrics
Private process metrics (e.g., defect rates by individual or module) are only known to by the individual or team concerned.
Public process metrics enable organizations to make strategic changes to improve the software process.
Metrics should not be used to evaluate the performance of individuals.
Statistical software process improvement helps and organization to discover where they are strong and where they are weak.
Statistical Process Control
Errors are categorized by their origin
Record cost to correct each error and defect
Count number of errors and defects in each category
Overall cost of errors and defects computed for each category
Identify category with greatest cost to organization
Develop plans to eliminate the most costly class of errors and defects or at least reduce their frequency
Project Metrics
A software team can use software project metrics to adapt project workflow and technical activities.
Project metrics are used to avoid development schedule delays, to mitigate potential risks, and to assess product quality on an on-going basis.
Every project should measure its inputs (resources), outputs (deliverables), and results (effectiveness of deliverables).
Software Measurement
Direct measures of a software engineering process include cost and effort.
Direct measures of the product include lines of code (LOC), execution speed, memory size, defects reported over some time period.
Indirect product measures examine the quality of the software product itself (e.g., functionality, complexity, efficiency, reliability, maintainability).
Size-Oriented Metrics
Derived by normalizing (dividing) any direct measure (e.g., defects or human effort) associated with the product or project by LOC.
Size-oriented metrics are widely used but their validity and applicability is a matter of some debate.
Function-Oriented Metrics
Function points are computed from direct measures of the information domain of a business software application and assessment of its complexity.
Once computed function points are used like LOC to normalize measures for software productivity, quality, and other attributes.
The relationship of LOC and function points depends on the language used to implement the software.
Object-Oriented Metrics
Number of scenario scripts (NSS)
Number of key classes (NKC)
Number of support classes (e.g., UI classes, database access classes, computations classes, etc.)
Average number of support classes per key class
Number of subsystems (NSUB)
Web Engineering Project Metrics
Number of static Web pages (Nsp)
Number of dynamic Web pages (Ndp)
Customization index: C = Nsp / (Ndp + Nsp)
Number of internal page links
Number of persistent data objects
Number of external systems interfaced
Number of static content objects
Number of dynamic content objects
Number of executable functions
Software Quality Metrics
Factors assessing software quality come from three distinct points of view (product operation, product revision, product modification).
Software quality factors requiring measures include
correctness (defects per KLOC)
maintainability (mean time to change)
integrity (threat and security)
usability (easy to learn, easy to use, productivity increase, user attitude)
Defect removal efficiency (DRE) is a measure of the filtering ability of the quality assurance and control activities as they are applied throughout the process framework
DRE = E / (E + D)
E = number of errors found before delivery of work product
D = number of defects found after work product delivery
Integrating Metrics with Software Process
Many software developers do not collect measures.
Without measurement it is impossible to determine whether a process is improving or not.
Baseline metrics data should be collected from a large, representative sampling of past software projects.
Getting this historic project data is very difficult, if the previous developers did not collect data in an on-going manner.
Metrics for Small Organizations
Most software organizations have fewer than 20 software engineers.
Best advice is to choose simple metrics that provide value to the organization and don't require a lot of effort to collect.
Even small groups can expect a significant return on the investment required to collect metrics, if this activity leads to process improvement.
Establishing a Software Metrics Program
Identify business goal
Identify what you want to know
Identify subgoals
Identify subgoal entities and attributes
Formalize measurement goals
Identify quantifiable questions and indicators related to subgoals
Identify data elements needed to be collected to construct the indicators
Define measures to be used and create operational definitions for them
