Chapter 4 Software Process and Project Metrics

Software metrics refers to a broad range of measurements for computer software. Measurement can be applied to the software process with the intent of improving it on a continuous basis. Measurement can be used throughout a software project to assist in estimation, quality control, productivity assessment, and project control. Measurement can be used by software engineers to help assess the quality of technical work products and to assist in tactical decision making as a project proceeds.

Why do we Measure? To characterize To evaluate To predict To improve

Measures, Metrics, and Indicators A measure provides a quantitative indication of the extent, amount, dimension, capacity, or size of some attribute of a product or process. Metrics is a quantitative measure of the degree to which a system, component, or process possesses a a given attribute.

Measures, Metrics, and Indicators An indicator is a metric or combination of metrics that provide insight into the software process, a software project, or the product itself. An indicator provides insight that enables the project manager or software engineers to adjust the process, the project, or the process to make things better.

Metrics in the Process and Project Domains Process indicators enable a software engineering organization to gain insight into the efficacy of an existing process (I.e., the paradigm, software engineering tasks, work products, and milestones). They enable managers and practitioners to assess what works and what doesn’t.

Metrics in the Process and Project Domains Project indicators enable a software project manager to assess the status of an ongoing project track potential risks Uncover problem areas before they go “critical” Adjust work flow or tasks, and Evaluate the project team’s ability to control quality of software work products

4.2.1 Process Metrics and Software Process Improvement Fig 4.1 We measure the efficacy of a software process indirectly; we derive a set of metrics based on the outcomes that can be derived from the process.

Process Metrics and Software Process Improvement A software metrics etiquette: Use common sense an 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 Cont..

Process Metrics and Software Process Improvement A software metrics etiquette (cont.): 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.

Process Metrics and Software Process Improvement A more rigorous approach: statistical software process improvement (SSPI): All errors and defects are categorized by origin (flaw in spec, flaw in logic, nonconformance to standards). The cost to correct each error and defect is recorded. The number of errors and defects in each category is counted and ranked in descending order. Cont..

Process Metrics and Software Process Improvement SPPI (cont.): 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 the 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. Fig 4.2 and Fig 4.3

4.2.2 Project Metrics Project metrics are used by a project manager and a software team to adapt project work flow and technical activities. Occurred during: estimation  monitor and control progress. production rates: pages of documentation, review hours, function points, and delivered source lines. errors technical metrics  quality

Project Metrics The first application of project metrics on most software projects occurs during estimation. Metrics collected from past projects are used as a basis from which effort and time estimates are made from current software work. Production rates are measured.

Project Metrics The intent of project metrics are two folds: to minimize the development schedule by making the adjustments necessary to avoid delays and mitigate potential problems. to assess product quality on an ongoing basis and, when necessary, modify the technical approach to improve quality.

Project Metrics Another model of project metrics suggests that every project should measure: Inputs – measures of the resources required to do the work Outputs – measures of the deliverables or work products created during the software engineering process Results – measures that indicate the effectiveness of the deliverables

Software Measurement Direct measures of SE process include cost and effort. Direct measures of product include LOC produced, execution speed, memory size, and defects reported over some set period of time. Indirect measures of product include functionality, quality, complexity, efficiency, reliability, maintainability, and many other “-abilities”

4.3.1 Size-oriented Metrics Derived by normalizing quality and/or productivity measures by considering the size of the software that has been produced. Fig 4.4 For example: choose LOC as normalization value.

Size-oriented Metrics Then we can develop a set of simple size-oriented metrics: Errors per KLOC Defects per KLOC $ per LOC Page of documentation per KLOC And other interesting metrics can be computed: Errors per person-month, LOC per person-month, $ per page of documentation.

4.3.2 Function-Oriented Metrics Use a measure of the functionality delivered by the application as a normalization value. Functionality can not be measured directly, it must be derived indirectly using other direct measures. A measure called the function point.

Function-Oriented Metrics Function points are derived using an empirical relationship based on countable (direct) measures of software's information domain and assessments of software complexity. Function points are computed by completing the table shown in Fig 4.5.

Computing Function Points

Function-Oriented Metrics To compute function points (FP), the following relationship is used: FP = count total x [0.65+0.01x(Fi)]

Analyzing the Information Domain

Taking Complexity into Account

Why Opt for FP Measures?

Typical Function-Oriented Metrics errors per FP defects per FP $ per FP pages of documentation per FP FP per person-month

4.4.3 Extended Function Point Metrics Function point was inadequate for many engineering and embedded systems. A function point extension called feature points, is a superset of the function point measure that can be applied to systems and engineering software applications. Accommodate applications in which algorithmic complexity is high.

Extended Function Point Metrics The feature point metric counts a new software characteristic – algorithms. Another function point extension – developed by Boeing  integrate data dimension of software with functional and control dimensions. “3D function point”. “Counted, quantified, and transformed”

Extended Function Point Metrics To compute 3D function points, use this relationship: Index = I + O + Q + F + E + T + R Where each complexity weighted value is computed using: Complexity weighted value = NilWil+NiaWia+NihWih Where Nil, Nia, Nih represent the number of occurrences of element I for each complexity; and Wil, Wia, and Wih are the corresponding weights.

Extended Function Point Metrics Function points, feature points, and 3D point represent the same thing – “functionality” or “utility” delivered by software.

4.4 Reconciling Different Metrics Approaches Attempt to relate FP and LOC measures. Table in page 94

4.5 Metrics for Software Quality Must use technical measures to evaluate quality in objective, rather than subjective ways. Must evaluate quality as the project progresses. The primary thrust is to measure errors and defects  metrics provide indication of the effectiveness software quality assurance and control activities.

Measuring Quality Correctness: defects per KLOC Maintainability: the ease that a program can be corrected, adapted, and enhanced. Time/cost. Time-oriented metrics: Mean-time-to-change (MTTC) Cost-oriented metrics: Spoilage – cost to correct defects encountered.

Measuring Quality Integrity: ability to withstand attacks Threat: the probability that an attack of a specific type will occur within a given time. Security: the probability that the attack of a specific type will be repelled. Integrity = sum [(1 – threat)x(1 – security)]

Measuring Quality Usability: attempt to quantify “user-friendliness” in terms of four characteristics: The physical/intellectual skill to learn the system The time required to become moderately efficient in the use of the system The net increase of productivity A subjective assessment of user attitude toward the system (e.g., use of questionnaire).

Defect Removal Efficiency A quality metric that provides benefit at both the project and process level. DRE is a measure of filtering ability of quality assurance and control activities as they applied throughout all process framework activities.

Defect Removal Efficiency DRE = (errors) / (errors + defects) where errors = problems found before release defects = problems found after release The ideal value for DRE is 1  no defects found.

Defect Removal Efficiency DRE is defined as: DRE = E/(E + D) Where E is the number of errors found before delivery of S/W to the end-user And D is the number of defects found after delivery The ideal value for DRE is 1  no defects found.

4.6 Integrating Metrics Within the Software Process Arguments for Software Metrics: Why is it so important to measure the process of software engineering and the product (software) that it produces?

4.7 Managing Variation: Statistical Process Control How can we compare a variety of different projects? Use of Control Chart: to determine whether the dispersion (variability) and “location” (moving average) of process metrics are stable or unstable. The moving average control chart The individual control chart Fig. 4.8 Page102

Moving Range (mR) Control Chart Calculate the moving ranges (mR) Calculate the mean of the moving ranges Multiply the mean by 3.268  upper control limit (UCL) Fig. 4.8  4.9 Are all moving range values inside the UCL? If “yes”  stable

Individual Chart Control Plot individual metrics values as shown in Fig 4.8 Compute the average value, Am Multiply the mean of the mR value by 2.660 and add Am in (2)  plot the upper natural process limit (UNPL) Multiply the mean of the mR value by 2.660 and subtract Am in (2)  plot the lower natural process limit (LNPL) Compute the SD as (UNPL – Am)/3. Plot lines one and two SD above and below Am.

Individual Chart Control Zone rules: If any of the following conditions is true, the metrics data is out of control: A single metrics value lies outside the UNPL Two out of three successive metrics values lie more than two SD away from Am Four out of five successive metric values lie more than one SD away from Am Eight consecutive metrics values lie on one side of Am.

4.8 Metrics for Small Organizations “Keep it simple”: Time Effort Errors Defects

Homework #2 Problem# 4.9, 4.11, 4.13, 4.17, and 4.18 Due Mon 15 July 2002