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Process Improvement

Process Improvement

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Process Improvement

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  1. Process Improvement CIS 376 Bruce R. Maxim UM-Dearborn

  2. Process Improvement Goals • Understanding existing processes • Introduce process changes to improve quality, reduce costs, or accelerate schedules • Industry is demanding increased attention to quality in general • Most process improvement work focuses on defect reduction and prevention • There are other process attributes that deserve our attention

  3. Process Improvement Attributes - part 1 • Understandability - degree to which a process is well defined and understood • Visibility - process activities have results that are externally recognizable • Supportability - process activities supported by CASE tools • Acceptability - defined processes are used and accepted by software engineers

  4. Process Improvement Attributes - part 2 • Reliability - process is defined so that errors are avoided or trapped before product errors result • Robustness - process can continue despite unexpected problems • Maintainability - process can evolve to reflect changing organizational requirements or identified process improvements • Rapidity - the time required to complete a system from specification to delivery

  5. Process Improvement Stages • Process analysis • modeling and quantitative analysis of existing processes • Improvement identification • quality, cost, and scheduling bottlenecks located • Process change introduction • modify process to remove bottlenecks • Process change training • train staff involved in process revision proposals • Change tuning • process improvements are revised and allowed to evolve

  6. Process Improvement Activities

  7. Process and Product Quality • Closely related to one another • Good processes are usually required to produce good products • In manufacturing applications, process is principle determinant of quality • For design-based activities, the capabilities of the designers are also important

  8. Product Quality Factors • Development technology • for large projects with average capability this is the main determinant of product quality • Quality of people involved • for small projects the developer capability is the main determinant of product quality • Process quality • significant for both small and large projects • Cost, time, and schedule constraints • unrealistic schedules can doom the quality of most products

  9. Process Analysis and Modeling • Process analysis • study of existing processes to understand relationships among process components • allows comparisons with other processes • Process modeling • documentation of process in which the tasks, roles, and entities used are recorded • best to represent models graphically • several different perspectives may be used (e.g. activities, deliverables, etc.) • model should be examined for weaknesses, this involves discussion with stakeholders

  10. Process Model Elements - part 1 • Activity - (round edged rectangle) • has clearly defined objective, entry, and exit conditions • Process - (round edged rectangle with shadow) • set of coherent activities with agreed upon objective • Deliverable - (rectangle with shadow) • tangible output of an activity predicted by project plan • Condition - (parallelogram) • process or activity pre- or post-conditions

  11. Process Model Elements - part 2 • Role - (circle with shadow) • defined and bounded area of responsibility • Exception - (double edged box)) • description of how to modify the process if anticipated or unanticipated events occur • Communication - (arrow) • exchange of information between people and/or machines

  12. Process Model Example

  13. Process Exceptions • Process models can’t represent how to handle exceptions • key people are lost prior to a critical review • failure of e-mail server for several days • organizational reorganization • request to respond to change requests • General procedure is to suspend the process model and follow RMMM plans augmented with the managers own initiatives

  14. Process Measurement • Wherever possible quantitative process data should be collected • Organizations without process standards may have to be define processes before measurements can be made (since they won’t know what to measure) • Process measurements should be used to assess process improvements • Organization objectives drive process improvement, not measurements

  15. Process Measurement Classes • Time taken to complete process activities • e.g. calendar time to complete a milestone • Resources required to complete processes or activities • e.g. person months • Number of event occurrences • e.g. number of defects found

  16. Goal Question Metric Paradigm • Goals • What is the organization trying to achieve? • Process improvement deals with goal satisfaction. • Questions • Concerned with areas of uncertainty related to goals. • You need process knowledge to derive questions. • Metrics • Measurements collected to answer questions

  17. SEI Process Maturity Model • Level 1 - Initial • essentially uncontrolled • Level 2 - Repeatable • project management procedures defined and used • Level 3 - Defined • process management strategies defined and used • Level 4 - Managed • quality management strategies defined and used • Level 5 - Optimizing • process improvement strategies defined and used

  18. SEI Process Model Problems • Focuses on project management rather than project development • Ignores the use of strategies like rapid prototyping • Model is intended to represent organizational capability and not practices used on particular projects • There may be wide variation in the practices used in a single organization • Capability assessment is questionnaire-based

  19. Capability Assessment Process

  20. Process Classification • Informal • No detailed process model, developers created their own way of doing things • Managed • defined model drive development process • Methodical • processes supported by standard development method • Supported • processes supported by automated CASE tools

  21. Process Tool Support

  22. Defect Removal Effectiveness • Defect removal is central to software development • One of the top expense items • Affects project scheduling • Improves product quality

  23. PSP - Defect Density • This is the primary defect measure used in PSP • Dd = 1000 * D/N • D = total number of defects found in all phases of the process • N = number of new and changed lines of code in the program

  24. Defect Density Example • For a program with 96 new or changed lines of code and 14 defects • Dd = 1000 * (14/96) = 145.83 defects/KLOC

  25. Defect Metrics - part 1 • Error Detection Efficiency 100%*(#errors found in 1 inspection)/(#errors in product before inspection) • Defect Removal Efficiency 100%*(#defects found now)/(#defects found now + #defects found later) • Error Detection Percentage 100%*(#inspection errors)/(#inspection errors + #valid discrepancy reports)

  26. Defect Metrics - part 2 • Total Defect Containment Effectiveness (TDCE) (#prerelease defects)/(#prerelease defects + #post-release defects) • Phase Containment Effectiveness (PCE) (#phase(i) defects)/(#phase(i) defects + #phase(i+x) defects) • Effectiveness (E) 100%*N/(N + S) N = #defects found by an activity S = #defects found in subsequent activities

  27. Phase-based Defect Removal Model • Defects present at exit of each development phase are estimated • This allows us to set realistic targets and assess the costs of reducing error injection rates • This is a quality management tool and not a device for estimation of software reliability • How would this work in practice?

  28. Assumptions • Suppose we decide to create two broad defect removal classes • activities that handle defects before code is integrated into the system library (design reviews, inspections, unit testing) • formal machine tests after code integration • Also assume the same defect removal effectiveness for each phase

  29. Example - part 1 • MP = major problems found in before integration • PTR = errors found during formal machine tests • mu = MP/PTR • the higher the value of mu the better • Q = defects found after release to customer • TD = (MP + PTR + Q) • total defects for life of software

  30. Example - part 2 • Phase 1 effectiveness E1 = MP/TD MP = E1 * TD • Phase 2 effectiveness E2 = PTR/(TD - MP) PTR = E2 * (TD - MP)

  31. Example - part 3 • Some equations that can be useful in quality planning (assuming that E1 = E2) Q = PTR /(mu - 1) Q = MP / [mu * (mu - 1)] Q = TD / (mu * mu) • These equations work with either raw or normalized defect values

  32. PSP – Phase Yield Phase yield = 100 * (defects removed during phase)/ (defects in product at phase entry) Note: cannot be computed until project is completed

  33. Phase Yield - Example • 5 defects found during code review • 3 defects found during compile • 2 defects found during unit testing • 2 defects found during integration testing • Phase yield for compile = 100 * 3 / (3 + 2 + 2) = 42.9 % • Phase yield for code review = 100 * 5 /(5 + 3 + 2 + 2) = 41.7 %

  34. Seven Basic Software Quality Tools • Checklists (paper forms) • used to gather data for later analysis • used to confirm that process tasks are complete • both simple yes/no and branching questions

  35. Seven Basic Software Quality Tools • Pareto Diagram • bar chart sorted in descending height order • vertical axis labeled with # defects • horizontal axis (nominal) labeled with defect cause types • software defects tends cluster near related causes

  36. Seven Basic Software Quality Tools • Histogram • frequency bar graph • vertical axis is # defects • horizontal axis has ordinal or interval type labels

  37. Seven Basic Software Quality Tools • Flowchart • pictorial representation of a process • breaks down process into its constituent steps • can be useful in identifying were errors are likely to be found in the system

  38. Seven Basic Software Quality Tools • Scatter diagram (point plots) • used with correlation, regression, or statistical modeling • vertical axis is # defects • horizontal axis some metric (e.g. McCabe’s index)

  39. Seven Basic Software Quality Tools • Run chart • line graph showing performance of dependent variable (y) over time (x) • best used for trend analysis (e.g. arrival of defects during formal machine testing) • can plot cumulative dependent variables (S curves)

  40. Seven Basic Software Quality Tools • Control chart • advanced form of run chart where capability is defined • upper and lower control limits (dashed lines) are drawn to alert the user when dependent measure is out of control • can plot cumulative dependent variables (S curves) • C chart based on # conforming or not • R chart based on subgroup ranges (max – min) • X bar chart based on subgroup means

  41. Control Chart (C)