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Software Metrics

Software Metrics. “How do we measure the software?”. Lecture Objectives. To understand the importance of measurement in software engineering To describe and compare the different metrics that can be used for measuring software

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Software Metrics

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  1. Software Metrics “How do we measure the software?” Software Engineering

  2. Lecture Objectives • To understand the importance of measurement in software engineering • To describe and compare the different metrics that can be used for measuring software • To understand the important factors that affect the measurement of software Software Engineering

  3. Engineering Method • Scientific • Precision • Accuracy • Repeatable • Controllable • Quality Software Engineering

  4. Why Do We Measure? • To indicate the quality of the product • To assess the productivity of the people who produce the product • To assess the benefits derived from new software engineering methods and tools • To form a baseline for estimation • To help justify requests for new tools or additional training Software Engineering

  5. Definitions • Measure - quantitative indication of some attribute of a product or process • Measurement - the act of determining a measure • Metric - a quantitative measure of the degree to which a system, component, or process possesses a given attribute (IEEE) Software Engineering

  6. What Can Be Measured? • Direct measures • Lines of codes (LOC), speed, cost, memory size, errors, ... • Indirect measures • Quality, functionality, complexity, reliability, efficiency, maintainability, ... Software Engineering

  7. Size-Oriented Metrics • Based on the “size” of the software produced • Project data measured, including cost and effort • Advantages: easily counted, large body of literature and data based on LOC • Disadvantages: language dependent, programmer dependent • Useful for projects with similar environment Software Engineering

  8. Example of Project Measurements Software Engineering

  9. Example of Size-Oriented Metrics • Productivity = Size / Effort = kLOC / person-month • Quality = Errors / Size = Errors / kLOC • Cost = $ / kLOC • Documentation = pages / kLOC Software Engineering

  10. Function-Oriented Metrics • Based on “functionality” delivered by the software • Functionality is measured indirectly • Function points (FP) - derived using an empirical relationship based on countable measures of software’s information domain and assessments of software complexity Software Engineering

  11. Software information domain values • Number of user inputs • Number of user outputs • Number of user inquiries • Number of files • Number of external interfaces Software Engineering

  12. Steps In Calculating FP 1. Count the information domain values. 2. Assess the complexity of the values. 3. Calculate the raw FP (see next table). 4. Rate the complexity factors to produce the complexity adjustment value (CAV) 5. Calculate the adjusted FP as follows: FP = raw FP x [0.65 + 0.01 x CAV] Software Engineering

  13. Function Point Metrics ParameterCountSimpleAverageComplex Inputs x 3 4 6 = Outputs x 4 5 7 = Inquiries x 3 4 6 = Files x 7 10 15 = Interfaces x 5 7 10 = Count-total (raw FP) Weighting Factor Software Engineering

  14. Rate Complexity Factors For each complexity adjustment factor, give a rating on a scale of 0 to 5 0 - No influence 1 - Incidental 2 - Moderate 3 - Average 4 - Significant 5 - Essential Software Engineering

  15. Complexity Adjustment Factors • Does the system require reliable backup and recovery? • Are data communications required? • Are there distributed processing functions? • Is performance critical? • Will the system run in an existing, heavily utilized operational environment? Software Engineering

  16. Complexity Adjustment Factors (Continued) • Does the system require on-line data entry? • Does the on-line data entry require the input transaction to be built over multiple screens or operations? • Are the master files updated on-line? • Are the inputs, outputs, files, or inquiries complex? Software Engineering

  17. Complexity Adjustment Factors (Continued) • Is the internal processing complex? • Is the code designed to be reusable? • Are conversion and installation included in the design? • Is the system designed for multiple installations in different organizations? • Is the application designed to facilitate change and ease of use by the user? Software Engineering

  18. Complexity Adjustment Value • The rating for all the factors, F1 to F14, are summed to produce the complexity adjustment value (CAV) • CAV is then used in the calculation of the function point (FP) of the software Software Engineering

  19. Example of Function-Oriented Metrics • Productivity = Functionality / Effort = FP / person-month • Quality = Errors / Functionality = Errors / FP • Cost = $ / FP • Documentation = pages / FP Software Engineering

  20. FP Characteristics • Advantages: language independent, based on data known early in project, good for estimation • Disadvantages: calculation complexity, subjective assessments, FP has no physical meaning (just a number) Software Engineering

  21. Extended Function Point Metrics • Feature points • applied to systems and engineering software • includes assessment for algorithms • 3D Function points • applied to real-time systems and engineered products (by Boeing) • integrates data dimension (normal FP) with functional and control dimensions Software Engineering

  22. Reconciling Metrics Approaches • A number of studies attempted to relate FP and LOC measures • Relationship depends on programming language and quality of design • Example: one LOC of Ada provides approximately 1.4 times as much “functionality” (on average) of one LOC of Fortran Software Engineering

  23. Programming Language Assembly Language C Cobol Fortran Pascal Ada Object-oriented language Fourth-generation language Code generators LOC/FP (average) 320 128 105 105 90 70 30 20 15 Comparison of LOC/FP Software Engineering

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