SE401 · Annotated SlidesQuality — Quality Assurance, Quality Control and Metrics
Quality · Annotated slides

Quality Assurance, Quality Control and Metrics

Definitions of software quality, quality assurance vs. quality control, the SQA group’s role, what makes projects succeed or fail, and software measurement: measures, metrics, product vs. process metrics, direct vs. indirect measures and size-oriented metrics.

Slide text Study notes Highlight: key words Tips, tricks & traps Seen again elsewhere How it can come up
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    Slide 2: • Software Quality • Quality Assurance
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      Software Quality

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        Slide 4: • A definition of quality should emphasize three important points:
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          Slide 5: • The purpose of software testing is to assess and evaluate the quality of work performed at each step of the software development process.
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            Slide 6: • A good development process, tools, methods, and people go far in providing quality products
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              Quality Assurance

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              1. Quality assurance (QA): PROCESS-oriented, preventive: make sure the right processes are followed so defects aren’t introduced.
              Slide 8: • Quality assurance (QA) activities strive to ensure:
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                Slide 9: • The following need to be considered: Scope, Stakeholders, Risks, Internal and External Environmental Factors, Process
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                  Quality Control

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                  1. Quality control (QC): PRODUCT-oriented, detective: inspect and test the product to find defects.
                  Memory trickQA = prevent (process). QC = detect (product). Testing is part of QC.
                  🎯 How this can come up · 1 question
                  MCQ

                  Testing a product to find defects is an example of:

                  Slide 11: Perform quality control is concerned with monitoring specific project results for compliance with quality standards
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                    Slide 12: • The group should prepare an SQA plan for a project. • The plan identifies
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                      Slide 13: • Participates in the development of the project’s software process description
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                        Project Success

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                          Slide 15: • Keep requirements tight & focused • One milestone at a time
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                            Slide 16: Too much medicine can kill the patient
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                              Slide 17: Be Disciplined and Professional
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                                Slide 18: • MBWA – Management by Walk About
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                                  Slide 19: 1. On schedule
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                                    Slide 20: • How to identify a project’s success potential
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                                      Slide 21: • Executive support • User involvement
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                                      1. Success factors: executive support, user involvement, experienced project manager, clear objectives, small scope, standard infrastructure.
                                      Slide 22: • Top management can help to:
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                                        Slide 23: • Factors that may influence the success or failure of the software projects could be:
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                                          Slide 24: Technologies on Unsuccessful Projects Technologies on Successful Projects
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                                            Slide 25: Social Factors on Unsuccessful Projects Social factors on Successful Projects
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                                              Slide 26: • Do the same things you did on the last project. Over and over and over.
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                                                Measurement and Metrics

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                                                  Slide 28: • “Not everything that can be counted counts, and not everything that counts can be counted.”
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                                                    Slide 29: • Software measurement is concerned with deriving a quantitative (numeric) value for an attribute of a software product or process
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                                                    1. Measure: a quantitative value of an attribute. Metric: a measure related to a goal (defects per KLOC).
                                                    Slide 30: • Measure
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                                                      Slide 31: • Any type of measurement which relates to a software system, process or related documentation
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                                                        Slide 32: • Product
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                                                        1. Product metrics (size, complexity, defects), process metrics (effort, defect-removal efficiency), project metrics (cost, schedule).
                                                        Slide 33: • Private process metrics (e.g., defect rates by individual or module) are only known to by the individual or team concerned.
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                                                          Slide 34: • A quality metric should be a predictor of product quality • Classes of product metric
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                                                            Slide 35: • A software team can use software project metrics to adapt project workflow and technical activities
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                                                              Slide 36: • A software property can be measured • The relationship exists between what we can measure and what we
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                                                                Slide 37: • Many software developers do not collect measures. • Without measurement it is impossible to determine whether a process
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                                                                  Slide 38: • Direct measures of a software engineering process include cost and effort
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                                                                  1. Direct measures: LOC, execution speed, defects. Indirect: functionality, quality, complexity, maintainability.
                                                                  Slide 39: • A software measurement process may be part of a quality control process
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                                                                      Slide 41: • A metrics program should be based on a set of product and process data
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                                                                        Slide 42: • Don’t collect unnecessary data
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                                                                            Slide 44: • It is not always obvious what data means
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                                                                              Slide 45: • Size-oriented metrics
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                                                                              1. Size-oriented metrics normalise by size: errors per KLOC, cost per KLOC, pages of documentation per KLOC.
                                                                              🎯 How this can come up · 1 question
                                                                              MCQ

                                                                              12 defects in 4,000 lines of code is: