Process and Project Metrics

 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).

Process and Project Metrics

·         Metrics should be collected so that process and product indicators can be ascertained

·         Process metrics used to provide indictors that lead to long term process improvement

·         Project metrics enable project manager to

1.    Assess status of ongoing project

2.    Track potential risks

3.    Uncover problem are before they go critical

4.    Adjust work flow or tasks

5.    Evaluate the project team’s ability to control quality of software work products

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 are week.

Statistical Process Control

1.     Errors are categorized by their origin

2.     Record cost to correct each error and defect

3.     Count number of errors and defects in each category

4.     Overall cost of errors and defects computed for each category

5.     Identify category with greatest cost to organization

6.     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 process measures include cost and effort.

·         Direct process measures 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 widely debated.

 

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.

Reconciling LOC and FP Metrics

·         The relationship between lines of code and function points depends upon the programming language that is used to implement the software and the quality of the design

·         Function points and LOC-based metrics have been found to be relatively accurate predictors of software development effort and cost

·         Using LOC and FP for estimation a historical baseline of information must be established.          

 

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)

 Use Case-Oriented Metrics

·         Describe (indirectly) user-visible functions and features in language independent manner

·         Number of use case is directly proportional to LOC size of application and number of test cases needed

·         However use cases do not come in standard sizes and use as a normalization measure is suspect

·         Use case points have been suggested as a mechanism for estimating effort

 WebApp 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

1.    Correctness (defects per KLOC)

2.    Maintainability (mean time to change)

3.    Integrity (threat and security)

4.    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.

Arguments for Software Metrics

·         If you don’t measure you have no way of determining any improvement

·         By requesting and evaluating productivity and quality measures software teams can establish meaningful goals for process improvement

·         Software project managers are concerned with developing project estimates, producing high quality systems, and delivering product on time

·         Using measurement to establish a project baseline helps to make project managers tasks possible

 

Baselines

·         Establishing a metrics baseline can benefit portions of the process, project, and product levels

·         Baseline data must often be collected by historical investigation of past project (better to collect while projects are on-going)

·         To be effective the baseline data needs to have the following attributes:

1.    data must be reasonably accurate, not guesstimates

2.    data should be collected for as many projects as possible

3.    measures must be consistent

4.    applications should be similar to work that is to be estimated

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

1.       Identify business goal

2.       Identify what you want to know

3.       Identify sub goals

4.       Identify sub goal entities and attributes

5.       Formalize measurement goals

6.       Identify quantifiable questions and indicators related to subgoals

7.       Identify data elements needed to be collected to construct the indicators

8.       Define measures to be used and create operational definitions for them

9.       Identify actions needed to implement the measures

10.    Prepare a plan to implement the measures