Notes

Outline

Chapter 7: Project Quality Management

Quality of Information Technology Projects Many people joke about the poor quality of IT products People seem to accept systems being down occasionally or needing to reboot their PCs There are many examples in the news about quality problems related to IT (See What Went Wrong?) But quality is very important in many IT projects

What Is Project Quality Management? The International Organization for Standardization (ISO) defines quality as the totality of characteristics of an entity that bear on its ability to satisfy stated or implied needs Other experts define quality based on conformance to requirements: meeting written specifications fitness for use: ensuring a product can be used as it was intended

Project Quality Management Processes Quality planning: identifying which quality standards are relevant to the project and how to satisfy them Quality assurance: evaluating overall project performance to ensure the project will satisfy the relevant quality standards Quality control: monitoring specific project results to ensure that they comply with the relevant quality standards while identifying ways to improve overall quality

Modern Quality Management Modern quality management requires customer satisfaction prefers prevention to inspection recognizes management responsibility for quality Noteworthy quality experts include Deming, Juran, Crosby, Ishikawa, Taguchi, and Feigenbaum

Quality Experts Deming was famous for his work in rebuilding Japan and his 14 points Juran wrote the Quality Control Handbook and 10 steps to quality improvement Crosby wrote Quality is Free and suggested that organizations strive for zero defects Ishikawa developed the concept of quality circles and using fishbone diagrams Taguchi developed methods for optimizing the process of engineering experimentation Feigenbaum developed the concept of total quality control

Figure 7-1. Sample Fishbone or Ishikawa Diagram

Malcolm Baldrige Award and ISO 9000 The Malcolm Baldrige Quality Award was started in 1987 to recognize companies with world-class quality ISO 9000 provides minimum requirements for an organization to meet their quality certification standards

Quality Planning It is important to design in quality and communicate important factors that directly contribute to meeting the customer’s requirements Design of experiments helps identify which variable have the most influence on the overall outcome of a process Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability

Quality Assurance Quality assurance includes all the activities related to satisfying the relevant quality standards for a project Another goal of quality assurance is continuous quality improvement Benchmarking can be used to generate ideas for quality improvements Quality audits help identify lessons learned that can improve performance on current or future projects

Quality Control The main outputs of quality control are acceptance decisions rework process adjustments Some tools and techniques include pareto analysis statistical sampling quality control charts testing

Pareto Analysis Pareto analysis involves identifying the vital few contributors that account for the most quality problems in a system Also called the 80-20 rule, meaning that 80% of problems are often due to 20% of the causes Pareto diagrams are histograms that help identify and prioritize problem areas

Figure 7-2. Sample Pareto Diagram

Statistical Sampling and Standard Deviation Statistical sampling involves choosing part of a population of interest for inspection The size of a sample depends on how representative you want the sample to be Sample size formula: Sample size = .25 X (certainty Factor/acceptable error)2

Table 7-1. Commonly Used Certainty Factors

Standard Deviation Standard deviation measures how much variation exists in a distribution of data A small standard deviation means that data cluster closely around the middle of a distribution and there is little variability among the data A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population

Figure 7-3. Normal Distribution and Standard Deviation

Table 7-2. Sigma and Defective Units

Quality Control Charts, Six Sigma, and the Seven Run Rule A control chart is a graphic display of data that illustrates the results of a process over time.  It helps prevent defects and allows you to determine whether a process is in control or out of control Operating at a higher sigma value, like 6 sigma, means the product tolerance or control limits have less variability The seven run rule states that if seven data points in a row are all below the mean, above,the mean, or increasing or decreasing, then the process needs to be examined for non-random problems

Figure 7-4. Sample Quality Control Chart

Figure 7-5. Reducing Defects with Six Sigma

Testing Many IT professionals think of testing as a stage that comes near the end of IT product development Testing should be done during almost every phase of the IT product development life cycle

Figure 7-6. Testing Tasks in the Software Development Life Cycle

Types of Tests A unit test is done to test each individual component (often a program) to ensure it is as defect free as possible Integration testing occurs between unit and system testing to test functionally grouped components System testing tests the entire system as one entity User acceptance testing is an independent test performed by the end user prior to accepting the delivered system

Figure 7-7. Gantt Chart for Building Testing into a Systems Development Project Plan

Improving Information Technology Project Quality Several suggestions for improving quality for IT projects include Leadership that promotes quality Understanding the cost of quality Focusing on organizational influences and workplace factors that affect quality Following maturity models to improve quality

Leadership “It is most important that top management be quality-minded. In the absence of sincere manifestation of interest at the top, little will happen below.” (Juran, 1945) A large percentage of quality problems are associated with management, not technical issues

The Cost of Quality The cost of quality is the cost of conformance or delivering products that meet requirements and fitness for use the cost of nonconformance or taking responsibility for failures or not meeting quality expectations

Table 7-3. Costs Per Hour of Downtime Caused by Software Defects

Five Cost Categories Related to Quality Prevention cost: the cost of planning and executing a project so it is error-free or within an acceptable error range Appraisal cost: the cost of evaluating processes and their outputs to ensure quality Internal failure cost: cost incurred to correct an identified defect before the customer receives the product External failure cost: cost that relates to all errors not detected and corrected before delivery to the customer Measurement and test equipment costs: capital cost of equipment used to perform prevention and appraisal activities

Organization Influences, Workplace Factors, and Quality Study by DeMarco and Lister showed that organizational issues had a much greater influence on programmer productivity than the technical environment or programming languages Programmer productivity varied by a factor of one to ten across organizations, but only by 21% within the same organization Study found no correlation between productivity and programming language, years of experience, or salary A dedicated workspace and a quiet work environment were key factors to improving programmer productivity

Maturity Models Maturity models are frameworks for helping organization improve their processes and systems Software Quality Function Deployment Model focuses on defining user requirements and planning software projects The Software Engineering Institute’s Capability Maturity Model provides a generic path to process improvement for software development Several groups are working on project management maturity models

Project Management Maturity Model 1. Ad-Hoc: The project management process is described as disorganized, and occasionally even chaotic. The organization has not defined systems and processes, and project success depends on individual effort. There are chronic cost and schedule problems. 2. Abbreviated: There are some project management processes and systems in place to track cost, schedule, and scope. Project success is largely unpredictable and cost and schedule problems are common. 3. Organized: There are standardized, documented project management processes and systems that are integrated into the rest of the organization. Project success is more predictable, and cost and schedule performance is improved. 4. Managed: Management collects and uses detailed measures of the effectiveness of project management. Project success is more uniform, and cost and schedule performance conforms to plan. 5. Adaptive: Feedback from the project management process and from piloting innovative ideas and technologies enables continuous improvement. Project success is the norm, and cost and schedule performance is continuously improving.