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

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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 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) = 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 / ( ) = 42.9 % Phase yield for code review = 100 * 5 /( ) = 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

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36 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

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

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40 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

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42 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)

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44 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)

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47 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

48 Control Chart (C)

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51 Seven Basic Software Quality Tools Cause and effect (fish bone) diagram –not widely used in software development, but can be useful –shows effect between quality variable and the factors affecting it

52 Fishbone Diagram


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