Software Engineering

1. Software Engineering Software Metrics James Gain (jgain@cs.uct.ac.za) http://people.cs.uct.ac.za/~jgain/courses/SoftEng/

2. Objectives • Introduce the necessity for software metrics • Differentiate between process, project and product metrics • Compare and contrast Lines-Of-Code (LOC) and Function Point (FP) metrics • Consider how quality is measured in Software Engineering • Describe statistical process control for managing variation between projects

3. Measurement & Metrics Against: Collecting metrics is too hard ... it’s too time consuming ... it’s too political ... they can be used against individuals ... it won’t prove anything For: In order to characterize, evaluate, predict and improve the process and product a metric baseline is essential. “Anything that you need to quantify can be measured in some way that is superior to not measuring it at all” Tom Gilb

4. Terminology • Measure: Quantitative indication of the extent, amount, dimension, or size of some attribute of a product or process. A single data point • Metrics: The degree to which a system, component, or process possesses a given attribute. Relates several measures (e.g. average number of errors found per person hour) • Indicators: A combination of metrics that provides insight into the software process, project or product • Direct Metrics: Immediately measurable attributes (e.g. line of code, execution speed, defects reported) • Indirect Metrics: Aspects that are not immediately quantifiable (e.g. functionality, quantity, reliability) • Faults: • Errors: Faults found by the practitioners during software development • Defects: Faults found by the customers after release

5. A Good Manager Measures process process metrics project metrics measurement product metrics product What do we use as a “Not everything that can be counted counts, and not everything that counts can be counted.” - Einstein basis? • size? • function?

6. Process Metrics • Focus on quality achieved as a consequence of a repeatable or managed process. Strategic and Long Term. • Statistical Software Process Improvement (SSPI). Error Categorization and Analysis: • All errors and defects are categorized by origin • The cost to correct each error and defect is recorded • The number of errors and defects in each category is computed • Data is analyzed to find categories that result in the highest cost to the organization • Plans are developed to modify the process • Defect Removal Efficiency (DRE). Relationship between errors (E) and defects (D). The ideal is a DRE of 1:

7. Project Metrics • Used by a project manager and software team to adapt project work flow and technical activities. Tactical and Short Term. • Purpose: • Minimize the development schedule by making the necessary adjustments to avoid delays and mitigate problems • Assess product quality on an ongoing basis • Metrics: • Effort or time per SE task • Errors uncovered per review hour • Scheduled vs. actual milestone dates • Number of changes and their characteristics • Distribution of effort on SE tasks

8. Product Metrics • Focus on the quality of deliverables • Product metrics are combined across several projects to produce process metrics • Metrics for the product: • Measures of the Analysis Model • Complexity of the Design Model • Internal algorithmic complexity • Architectural complexity • Data flow complexity • Code metrics

9. Metrics Guidelines • Use common sense and organizational sensitivity when interpreting metrics data • Provide regular feedback to the individuals and teams who have worked to collect measures and metrics. • Don’t use metrics to appraise individuals • Work with practitioners and teams to set clear goals and metrics that will be used to achieve them • Never use metrics to threaten individuals or teams • Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement • Don’t obsess on a single metric to the exclusion of other important metrics

10. Normalization for Metrics • How does an organization combine metrics that come from different individuals or projects? • Depend on the size and complexity of the projec • Normalization: compensate for complexity aspects particular to a product • Normalization approaches: • Size oriented (lines of code approach) • Function oriented (function point approach)

11. Typical Normalized Metrics • Size-Oriented: • errors per KLOC (thousand lines of code), defects per KLOC, R per LOC, page of documentation per KLOC, errors / person-month, LOC per person-month, R / page of documentation • Function-Oriented: • errors per FP, defects per FP, R per FP, pages of documentation per FP, FP per person-month

12. Why Opt for FP Measures? • Independent of programming language. Some programming languages are more compact, e.g. C++ vs. Assembler • Use readily countable characteristics of the “information domain” of the problem • Does not “penalize” inventive implementations that require fewer LOC than others • Makes it easier to accommodate reuse and object-oriented approaches • Original FP approach good for typical Information Systems applications (interaction complexity) • Variants (Extended FP and 3D FP) more suitable for real-time and scientific software (algorithm and state transition complexity)

13. Computing Function Points Analyze information domain of the application and develop counts Establish count for input domain and system interfaces Assign level of complexity (simple, average, complex) or weight to each count Weight each count by assessing complexity Assess the influence of global factors that affect the application Grade significance of external factors, F_i, such as reuse, concurrency, OS, ... FP = SUM(count x weight) x C where complexity multiplier C = (0.65+0.01 x N) degree of influence N = SUM(F_i) Compute function points

14. Analyzing the Information Domain

15. Taking Complexity into Account • Complexity Adjustment Values (F_i) are rated on a scale of 0 (not important) to 5 (very important): • Does the system require reliable backup and recovery? • Are data communications required? • Are there distributed processing functions? • Is performance critical? • System to be run in an existing, heavily utilized environment? • Does the system require on-line data entry? • On-line entry requires input over multiple screens or operations? • Are the master files updated on-line? • Are the inputs, outputs, files, or inquiries complex? • Is the internal processing complex? • Is the code designed to be reusable? • Are conversion and instillation included in the design? • Multiple installations in different organizations? • Is the application designed to facilitate change and ease-of-use?

16. Exercise: Function Points • Compute the function point value for a project with the following information domain characteristics: • Number of user inputs: 32 • Number of user outputs: 60 • Number of user enquiries: 24 • Number of files: 8 • Number of external interfaces: 2 • Assume that weights are average and external complexity adjustment values are not important. • Answer:

17. Example: SafeHome Functionality Test Sensor Password SafeHome System User Sensors Zone Setting Zone Inquiry Messages Sensor Inquiry User Sensor Status Panic Button (De)activate (De)activate Monitor and Response System Password, Sensors, etc. Alarm Alert System Config Data

18. Example: SafeHome FP Calc weighting factor count simple avg. complex measurement parameter 9 3 number of user inputs X 3 4 6 = 8 2 number of user outputs X 4 5 7 = number of user inquiries 2 6 X 3 4 6 = 1 7 number of files X 7 10 15 = 22 4 number of ext.interfaces X 5 7 10 = 52 count-total 1.11 complexity multiplier 58 function points

19. Exercise: Function Points • Compute the function point total for your project. Hint: The complexity adjustment values should be low ( ) • Some appropriate complexity factors are (each scores 0-5): • Is performance critical? • Does the system require on-line data entry? • On-line entry requires input over multiple screens or operations? • Are the inputs, outputs, files, or inquiries complex? • Is the internal processing complex? • Is the code designed to be reusable? • Is the application designed to facilitate change and ease-of-use?

20. OO Metrics: Distinguishing Characteristics • The following characteristics require that special OO metrics be developed: • Encapsulation — Concentrate on classes rather than functions • Information hiding — An information hiding metric will provide an indication of quality • Inheritance — A pivotal indication of complexity • Abstraction — Metrics need to measure a class at different levels of abstraction and from different viewpoints • Conclusion: the class is the fundamental unit of measurement

21. OO Project Metrics • Number of Scenario Scripts (Use Cases): • Number of use-cases is directly proportional the number of classes needed to meet requirements • A strong indicator of program size • Number of Key Classes (Class Diagram): • A key class focuses directly on the problem domain • NOT likely to be implemented via reuse • Typically 20-40% of all classes are key, the rest support infrastructure (e.g. GUI, communications, databases) • Number of Subsystems (Package Diagram): • Provides insight into resource allocation, scheduling for parallel development and overall integration effort

22. OO Analysis and Design Metrics • Related to Analysis and Design Principles • Complexity: • Weighted Methods per Class (WMC): Assume that n methods with cyclomatic complexity are defined for a class C: • Depth of the Inheritance Tree (DIT): The maximum length from a leaf to the root of the tree. Large DIT leads to greater design complexity but promotes reuse • Number of Children (NOC): Total number of children for each class. Large NOC may dilute abstraction and increase testing

23. Further OOA&D Metrics • Coupling: • Coupling between Object Classes (COB): Total number of collaborations listed for each class in CRC cards. Keep COB low because high values complicate modification and testing • Response For a Class (RFC): Set of methods potentially executed in response to a message received by a class. High RFC implies test and design complexity • Cohesion: • Lack of Cohesion in Methods (LCOM): Number of methods in a class that access one or more of the same attributes. High LCOM means tightly coupled methods

24. OO Testability Metrics • Encapsulation: • Percent Public and Protected (PAP): Percentage of attributes that are public. Public attributes can be inherited and accessed externally. High PAP means more side effects • Public Access to Data members (PAD): Number of classes that access another classes attributes. Violates encapsulation • Inheritance: • Number of Root Classes (NRC): Count of distinct class hierarchies. Must all be tested separately • Fan In (FIN): The number of superclasses associated with a class. FIN > 1 indicates multiple inheritance. Must be avoided • Number of Children (NOC) and Depth of Inheritance Tree (DIT): Superclasses need to be retested for each subclass

25. Quality Metrics • Measures conformance to explicit requirements, following specified standards, satisfying of implicit requirements • Software quality can be difficult to measure and is often highly subjective • Correctness: • The degree to which a program operates according to specification • Metric = Defects per FP • Maintainability: • The degree to which a program is amenable to change • Metric = Mean Time to Change. Average time taken to analyze, design, implement and distribute a change

26. Quality Metrics: Further Measures • Integrity: • The degree to which a program is impervious to outside attack • Summed over all types of security attacks, i, where t = threat (probability that an attack of type i will occur within a given time) and s = security (probability that an attack of type i will be repelled) • Usability: • The degree to which a program is easy to use. • Metric = (1) the skill required to learn the system, (2) the time required to become moderately proficient, (3) the net increase in productivity, (4) assessment of the users attitude to the system • Covered in HCI course

27. P R O D U C T R E V I S I O N P R O D U C T R E V I S I O N P R O D U C T T R A N S I T I O N P R O D U C T T R A N S I T I O N P R O D U C T O P E R A T I O N P R O D U C T O P E R A T I O N Quality Metrics: McCall’s Approach Maintainability Portability Flexibility Reusability Testability Interoperability Correctness Usability Efficiency Reliability Integrity McCall’s Triangle of Quality

28. Quality Metrics: Deriving McCall’s Quality Metrics • Assess a set of quality factors on a scale of 0 (low) to 10 (high) • Each of McCall’s Quality Metrics is a weighted sum of different quality factors • Weighting is determined by product requirements • Example: • Correctness = Completeness + Consistency + Traceability • Completeness is the degree to which full implementation of required function has been achieved • Consistency is the use of uniform design and documentation techniques • Traceability is the ability to trace program components back to analysis • This technique depends on good objective evaluators because quality factor scores can be subjective

29. Managing Variation • How can we determine if metrics collected over a series of projects improve (or degrade) as a consequence of improvements in the process rather than noise? • Statistical Process Control: • Analyzes the dispersion (variability) and location (moving average) • Determine if metrics are: • (a) Stable (the process exhibits only natural or controlled changes) or • (b) Unstable (process exhibits out of control changes and metrics cannot be used to predict changes)

30. Control Chart + std. dev. • Compare sequences of metrics values against mean and standard deviation. e.g. metric is unstable if eight consecutive values lie on one side of the mean. Mean - std. dev.