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  • SPC is a quantitative technique that has been applied in manufacturing to improve process quality and thereby improving product quality. The strengths of SPC are that it provides easy to use graphical tools that enable the manager to visualize the performance of the manufacturing process. Underlying SPC is the notion that almost all the characteristics of processes and products display variation when measured over time. This variation has two sources (Florac 1997): Total variation = common cause variation + assignable cause variation where, common cause variation: variation that stems from natural phenomena that are inherent to the process. assignable cause variation: variation that have assignable causes that could have been prevented.
  • Process stability is at the core of process management. It refers to a predictable ‘in control’ process where the causes for all variations are attributed to common (natural) causes. On the other hand, process capability refers to the capability of a process to satisfy the requirements of the customer “voice of the customer”
  • Control charts are at the center of statistical process control. Control charts allow a software manager to visually assess a software process for stability and capability. A typical control chart plots the measured averages of a quality attribute from the process over time. A center line on the chart represents the process average of the quality attribute that correspond to the stable state. An upper control (UCL) and a lower control (LCL) limits on the quality attribute are also plotted. The quality attribute must fall within this range if the process is to be in control. In depth discussion of various control chart methods can be found in (Montgomery 1997, Quesenberry 1997).
  • This is another example of a control chart. According to this chart, although the process is stable (in control), it is not capable as it does not satisfy the requirements of the customer. This is evident by the measurements that lie outside the UL and LL lines representing customer requirements.
  • In the first scenario, the SPC component of the model is used to evaluate a base scenario. This is the scenario representing the current state of the process and is the scenario used as a benchmark for the study. To calculate the control limits the model is run for 10 times, with 6 iterations in each run resulting in a total of 60 observations. Notice that in the absence of a simulation model, this may not be possible. In effect, in the field, there are no guarantees regarding the stochastic properties of the inputs from one iteration to the next, let alone one CSCI to the next. A simulation model allows for calculating more reliable control limits.
  • In the second scenario the variability of the input scenarios is increased, thereby affecting the capability of the process. In effect, by increasing the variability of the input scenarios the “voice of the process” (wider control limits) is increased as shown on the slide.
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    1. 1. INFS 724 Project and Change Management Amit V. Deokar, Ph.D.
    2. 2. Chapter 8 Project Quality Management
    3. 3. Outline <ul><li>What is project quality management? Why? </li></ul><ul><li>Quality planning </li></ul><ul><li>Perform quality assurance </li></ul><ul><li>Perform quality control </li></ul><ul><li>Tools and techniques </li></ul><ul><ul><li>Pareto analysis </li></ul></ul><ul><ul><li>Statistical sampling </li></ul></ul><ul><ul><li>Six Sigma </li></ul></ul><ul><ul><li>Control charts </li></ul></ul><ul><ul><li>Testing </li></ul></ul><ul><ul><li>Ishikawa and the Fishbone diagram </li></ul></ul><ul><li>Using software for project quality management </li></ul>
    4. 4. What is project quality management? Why?
    5. 5. Examples of quality problems related to IT <ul><li>In July 2004 newspapers reported that a new government welfare management system in Canada costing several hundred million dollars was unable to handle a simple benefits rate increase after being put into live operation. Reportedly the original contract allowed for only 6 weeks of acceptance testing and the system was never tested for its ability to handle a rate increase. </li></ul><ul><li>A major U.S. retailer was reportedly hit with a large government fine in October of 2003 due to web site errors that enabled customers to view one another customers' online orders. </li></ul><ul><li>According to news reports in April of 2004, a software bug was determined to be a major contributor to the 2003 Northeast blackout , the worst power system failure in North American history. The failure involved loss of electrical power to 50 million customers , forced shutdown of 100 power plants, and economic losses estimated at $6 billion . The bug was reportedly in one utility company's vendor-supplied power monitoring and management system, which was unable to correctly handle and report on an unusual confluence of initially localized events. The error was found and corrected after examining millions of lines of code. </li></ul><ul><li>See other examples at http://www.softwareqatest.com/qatfaq1.html#FAQ1_3 </li></ul><ul><li>In July 2004 newspapers reported that a new government welfare management system in Canada costing several hundred million dollars was unable to handle a simple benefits rate increase after being put into live operation. Reportedly the original contract allowed for only 6 weeks of acceptance testing and the system was never tested for its ability to handle a rate increase. </li></ul><ul><li>A major U.S. retailer was reportedly hit with a large government fine in October of 2003 due to web site errors that enabled customers to view one another customers' online orders. </li></ul><ul><li>According to news reports in April of 2004, a software bug was determined to be a major contributor to the 2003 Northeast blackout , the worst power system failure in North American history. The failure involved loss of electrical power to 50 million customers , forced shutdown of 100 power plants, and economic losses estimated at $6 billion . The bug was reportedly in one utility company's vendor-supplied power monitoring and management system, which was unable to correctly handle and report on an unusual confluence of initially localized events. The error was found and corrected after examining millions of lines of code. </li></ul><ul><li>See other examples at http://www.softwareqatest.com/qatfaq1.html#FAQ1_3 </li></ul>
    6. 6. Costs Per Hour of Downtime Caused by Software Defects
    7. 7. The Cost of Quality <ul><li>The cost of quality is </li></ul><ul><ul><li>the cost of conformance or delivering products that meet requirements and fitness for use </li></ul></ul><ul><ul><li>the cost of nonconformance or taking responsibility for failures or not meeting quality expectations </li></ul></ul>
    8. 8. Five Cost Categories Related to Quality <ul><li>Conformance cost </li></ul><ul><ul><li>Prevention cost : the cost of planning and executing a project so it is error-free or within an acceptable error range </li></ul></ul><ul><ul><li>Appraisal cost : the cost of evaluating processes and their outputs to ensure quality </li></ul></ul><ul><ul><li>Measurement and test equipment costs : capital cost of equipment used to perform prevention and appraisal activities </li></ul></ul><ul><li>Nonconformance </li></ul><ul><ul><li>Internal failure cost : cost incurred to correct an identified defect before the customer receives the product </li></ul></ul><ul><ul><li>External failure cost : cost that relates to all errors not detected and corrected before delivery to the customer </li></ul></ul>
    9. 9. What Is Quality? <ul><li>Quality is a subjective term </li></ul><ul><li>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 </li></ul><ul><li>Other experts define quality based on </li></ul><ul><ul><li>conformance to requirements: meeting written specifications </li></ul></ul><ul><ul><li>fitness for use: ensuring a product can be used as it was intended </li></ul></ul>
    10. 10. Project Quality Management Processes <ul><li>Quality planning : identifying which quality standards are relevant to the project and how to satisfy them </li></ul><ul><li>Perform quality assurance : applying the planned, systematic quality activities to ensure that the project employs all processes needed to meet requirements </li></ul><ul><li>Perform quality control : monitoring specific project results to ensure that they comply with the relevant quality standards while identifying ways to improve overall quality </li></ul>
    11. 11. Quality planning
    12. 12. Quality Planning <ul><li>Quality is planned and not inspected </li></ul><ul><li>Quality planning involves identifying which quality standards are relevant to the project and how to satisfy them </li></ul><ul><li>Sometimes referred to as quality assurance </li></ul><ul><li>Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability </li></ul>
    13. 13. Quality management plan <ul><li>Sometimes referred to as ‘Quality Assurance Plan’ </li></ul><ul><li>It describes the project quality system: “the organizational structure, responsibilities, procedures, processes, and resources needed to implement quality management” (ISO 9000) </li></ul><ul><li>Examples: </li></ul><ul><ul><li>Software Quality Assurance Plan Example </li></ul></ul><ul><ul><li>Quality Assurance Plan Template 1 </li></ul></ul><ul><ul><li>Quality Assurance Plan Template 2 </li></ul></ul>
    14. 14. Perform quality assurance
    15. 15. Perform Quality Assurance <ul><li>It is the application of all planned and systematic activities to ensure that the project will employ all processes needed to meet requirements </li></ul><ul><li>Input </li></ul><ul><ul><li>Quality management plan </li></ul></ul><ul><ul><li>Quality metrics </li></ul></ul><ul><ul><li>Quality control measurements </li></ul></ul><ul><li>Tools: </li></ul><ul><ul><li>Quality planning and control tools and techniques (to be discussed) </li></ul></ul><ul><ul><li>Quality audits </li></ul></ul><ul><ul><li>Process analysis </li></ul></ul><ul><li>Output: Requested changes and corrective actions </li></ul>
    16. 16. Quality audits <ul><li>It is a structured review of other quality management activities. </li></ul><ul><li>The objective is to identify lessons learned that can improve the performance of the project </li></ul><ul><li>In essence, quality audits are meta quality management activities </li></ul>
    17. 17. Perform quality control
    18. 18. Quality Control <ul><li>It involves monitoring specific project results to determine if they comply with relevant quality standards. </li></ul><ul><li>Inputs </li></ul><ul><ul><li>Quality management plan </li></ul></ul><ul><ul><li>Quality metrics </li></ul></ul><ul><ul><li>Deliverables </li></ul></ul><ul><li>The main outputs of quality control are </li></ul><ul><ul><li>Validated defect repair </li></ul></ul><ul><ul><li>Validated deliverables </li></ul></ul><ul><li>Some tools and techniques include </li></ul><ul><ul><li>Inspection </li></ul></ul><ul><ul><li>Pareto analysis </li></ul></ul><ul><ul><li>statistical sampling </li></ul></ul><ul><ul><li>quality control charts </li></ul></ul>
    19. 19. Tools and techniques Pareto Analysis
    20. 20. Pareto Analysis <ul><li>Pareto analysis involves identifying the vital few contributors that account for the most quality problems in a system </li></ul><ul><li>Also called the 80-20 rule, meaning that 80% of problems are often due to 20% of the causes </li></ul><ul><li>Pareto diagrams are bar graphs used to arrange information in such a way that priorities for process improvement can be established. </li></ul>
    21. 21. Sample Pareto Diagram
    22. 22. Steps for creating Pareto diagrams <ul><li>Collect data on defects (quality problems) </li></ul><ul><li>Order by frequency of occurrence </li></ul><ul><li>Plot as a histogram </li></ul>1 2 3
    23. 23. Tools and techniques Statistical sampling
    24. 24. Statistical Sampling and Standard Deviation <ul><li>Statistical sampling involves choosing part of a population of interest for inspection </li></ul><ul><li>The size of a sample depends on how representative you want the sample to be </li></ul><ul><li>Sample size formula: </li></ul><ul><ul><li>Sample size = .25 X (certainty Factor/acceptable error) 2 </li></ul></ul>
    25. 25. Commonly Used Certainty Factors 95% certainty: Sample size = 0.25 X (1.960/.05) 2 = 384 90% certainty: Sample size = 0.25 X (1.645/.10) 2 = 68 80% certainty: Sample size = 0.25 X (1.281/.20) 2 = 10
    26. 26. Standard Deviation <ul><li>A small standard deviation means that data cluster closely around the middle of a distribution and there is little variability among the data </li></ul><ul><li>A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population </li></ul>
    27. 27. Normal Distribution and Standard Deviation
    28. 28. Tools and techniques Statistical process control
    29. 29. Quality Control Charts and the Seven Run Rule <ul><li>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 </li></ul><ul><li>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 </li></ul>
    30. 30. Sample Quality Control Chart
    31. 31. Statistical process control (SPC) <ul><li>SPC provides easy to use graphical tools for visualizing process performance </li></ul><ul><li>Underlying premise: all characteristics of process and products display variation when measured over time. </li></ul><ul><li>Total variation = common variation + assignable cause variation </li></ul>
    32. 32. SPC – Process stability versus capability <ul><li>A stable process is a predictable ‘in control’ process where the causes for all variations are attributed to common (natural) causes. </li></ul><ul><li>A capable process is a process capable of satisfying the requirements of the customer. </li></ul>
    33. 33. SPC – Stable and capable process
    34. 34. An SPC example – Stable but NOT capable process
    35. 35. A case study – Scenario #1
    36. 36. A case study – Scenario #2
    37. 37. Tools and techniques Inspection and Testing
    38. 38. Inspection and testing - Defined <ul><li>Inspection: Analyzes and checks system representations such as requirement documents, design diagrams, and program source code </li></ul><ul><li>Testing: Involves executing an implementation of the software with test data and examining the outputs and its operational behavior </li></ul>
    39. 39. Inspection effectiveness <ul><li>Inspections find 60-90% of defects in a program </li></ul><ul><li>Inspections have been found to produce net schedule savings of 10 – 30% </li></ul><ul><li>In one study, each hour spent on inspection avoided on average 33 hours of maintenance </li></ul><ul><li>Code reading detected about twice as many defects per hour of effort as testing </li></ul>
    40. 40. Testing effectiveness <ul><li>Unit testing finds anywhere from 10 to 50% of defects in a program </li></ul><ul><li>System testing finds from 20 to 60% of defects </li></ul><ul><li>Together, their defect detection rate is < 60% </li></ul>
    41. 41. Types of Tests <ul><li>A unit test is done to test each individual component (often a program) to ensure it is as defect free as possible </li></ul><ul><li>Integration testing occurs between unit and system testing to test functionally grouped components </li></ul><ul><li>System testing tests the entire system as one entity </li></ul><ul><li>Stress testing test the system for emergent properties such as performance and reliability </li></ul><ul><li>User acceptance testing is an independent test performed by the end user prior to accepting the delivered system </li></ul>
    42. 42. Testing Tasks in the Software Development Life Cycle
    43. 43. Tools and techniques Fishbone or Ishikawa Diagram
    44. 44. Fishbone or Ishikawa Diagram <ul><li>The Cause and Effect diagram also known as the &quot;fishbone&quot; or &quot;Ishikawa&quot; diagram after its creator Kaoru Ishikawa is used to systematically list all the different causes that can be attributed to a specific problem (or effect). </li></ul><ul><li>It helps identify, sort, and display possible causes of a specific problem or quality characteristic </li></ul><ul><li>It can help identify the reasons why a process goes out of control. </li></ul>
    45. 45. Sample Fishbone or Ishikawa Diagram
    46. 46. Basic layout of cause-and-effect diagrams
    47. 47. Steps for creating cause-effect diagram <ul><li>Identify and clearly define the outcome or EFFECT to be analyzed </li></ul><ul><li>Draw the SPINE and create the EFFECT box </li></ul>
    48. 48. Steps for creating cause-effect diagram (cont.) <ul><li>Identify the main CAUSES contributing to the effect being studied. These are the labels for the major branches of your diagram and become categories under which to list the many causes related to those categories. Examples of categories: </li></ul><ul><ul><li>3Ms and P - methods, materials, machinery, and people </li></ul></ul><ul><ul><li>4Ps - policies, procedures, people, and plant </li></ul></ul><ul><ul><li>Environment - a potentially significant fifth category </li></ul></ul>
    49. 49. Steps for creating cause-effect diagram (cont.) <ul><li>For each major branch, identify other specific factors which may be the CAUSES of the EFFECT </li></ul>
    50. 50. Steps for creating cause-effect diagram (cont.) <ul><li>Identify increasingly more detailed levels of causes and continue organizing them under related causes or categories. You can do this by asking a series of why questions. </li></ul><ul><li>Analyze the diagram </li></ul>
    51. 51. Using Software to Assist in Project Quality Management <ul><li>Spreadsheet and charting software helps create Pareto diagrams, Fishbone diagrams, etc. </li></ul><ul><li>Statistical software packages help perform statistical analysis </li></ul><ul><li>Specialized software products help manage Six Sigma projects or create quality control charts </li></ul><ul><li>Project management software helps create Gantt charts and other tools to help plan and track work related to quality management </li></ul>

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