Product Metrics SEII-Lecture 23

1. Product MetricsSEII-Lecture 23 Dr. Muzafar Khan Assistant Professor Department of Computer Science CIIT, Islamabad.

2. Recap • Measurement and quality assessment • Framework for product metrics • Measure, measurement, and metrics • Formulation, collection, analysis, interpretation, feedback • Principles for metrics characterization and validation • Metrics for requirements model • Function-based metrics • Metrics for specification quality • Metric for design model • Architectural design metrics • Metric for object-oriented design

3. Class-Oriented Metrics [1/3] • Weighted methods per class (WMC) • n methods of complexity c1, c2, … cn for a class C • WMC = ∑ci for i = 1 to n • If complexity increases, more efforts are required • Limited reuse • Counting methods apparently seems straightforward • Consistent counting approach is required • Depth of the inheritance tree (DIT) • Maximum length from the node to the root • If DIT grows, lower-level classes inherit many methods • Many methods may be reused • Leads to design complexity

4. Class-Oriented Metrics [2/3] • Number of Children (NOC) • Immediate subordinate classes • NOC grows • Reuse increases • Abstraction of parent class may be diluted • Testing effort increases • Coupling between class objects (CBO) • If coupling increases • Reusability decreases • Testing and modification complicated • CBO as low as reasonable

5. Class-Oriented Metrics [3/3] • Response for a class (RFC) • Methods potentially executed in response to message received by a class object • RFC increases, design complexity and testing increases • Lack of cohesion in methods (LCOM) • Number of methods that access one or more of the same attributes • If no methods access same attribute, LCOM is zero • If LCOM high, complexity of design increases

6. Component-Level Design Metrics [1/3] • Metrics for conventional components focus on internal characteristics of a component • Cohesion metrics • Data slice • Backward walk through a module to look data values • Data tokens • Variables defined • Glue tokens • Data tokens lies on data slice • Superglue tokens • Data tokens common to every data slice • Stickiness • The relative stickiness of glue token directly proportional to the number of data slices that it binds

7. Component-Level Design Metrics [2/3] • Coupling metrics • Data and control flow coupling • di = number of input data parameters • ci= number of input control parameters • do= number of output data parameters • co= number of output control data parameters • Global coupling • gd= number of global variable used as data • gc= number of global variable used as control • Environmental coupling • w = number of modules called • r = number of modules calling the module • mc = k/M where M = di+ (a * ci) + do+ (b * co) + gd+ (c * gc) + w + r

8. Component-Level Design Metrics [3/3] • Complexity metrics • Cyclomatic complexity • Number of independent logical paths • Variations of cyclomatic complexity

9. Operation-Oriented Metrics • Average operation size (OSavg) • Number of lines of code or number of messages sent by the operation • If number of messages sent increases, most probably responsibilities are not well allocated within a class • Operation complexity (OC) • Complexity metrics for conventional software • OC should be kept as low as possible • Average number of parameters per operation (NPavg) • Larger number of parameters, complex collaboration • NPavg should be kept as low as possible

10. Design Metrics for WebApps – Interface Metrics [1/2] • Layout complexity • Number of distinct regions defined for an interface • Layout region complexity • Average number of distinct links per region • Recognition complexity • Average number of distinct items the user must look at before making navigation or data input decision • Recognition time • Average time (in seconds) that it takes a user to select the appropriate action for a given task • Typing effort • Average number of key strokes required for a specific function

11. Interface Metrics [2/2] • Mouse pick effort • Average number of mouse picks per function • Selection complexity • Average number of links that can be selected per page • Content acquisition time • Average number of words of text per web page • Memory load • Average number of distinct data items that the user must remember to achieve specific objective

12. Aesthetic Design Metrics [1/2] • Word count • Total number of words that appear on a page • Body text percentage • Percentage of words that are body versus display text (i.e. headers) • Emphasized body text % • Portion of body text that is emphasized (e.g., bold, capitalized) • Text cluster count • Text areas highlighted with color, bordered regions, rules, or lists • Link count • Total links on a page

13. Aesthetic Design Metrics [2/2] • Page size • Total bytes for the page as well as elements, graphics, and style sheets • Graphic percentage • Percentage of page bytes that are for graphics • Graphics count • Total graphics on a page (not including graphics specified in scripts, applets, and objects) • Color count • Total colors employed • Font count • Total fonts employed (i.e. face + size + bold + italic)

14. Content Metrics • Page wait • Average time required for a page to download at different connection speeds • Page complexity • Average number of different types of media used on page, not including text • Graphic complexity • Average number of graphics media per page • Audio complexity • Average number of audio media per page • Video complexity • Average number of video media per page • Animation complexity • Average number of animations per page • Scanned image complexity • Average number of scanned images per page

15. Navigation Metrics • For static pages • Page-linking complexity • Number of links per page • Connectivity • Total number of internal links, not including dynamically generated links • Connectivity density • Connectivity divided by page count

16. Metrics for Source Code • n1 = number of distinct operators that appear in a program • n2 = number of distinct operands that appear in a program • N1 = total number of operator occurrences • N2 = total number of operand occurrences • Overall program length (N) and volume (V) N = n1log2n1 + n2 log2n2 V = N log2 (n1+ n2)

17. Metrics for Object-Oriented Testing [1/2] • Lack of cohesion in methods (LCOM) • If LCOM is high, more states must be tested • Percent public and protected (PAP) • Percentage of public and protected class attributes • High value for PAP increases the side effects among classes • Public access to data members (PAD) • The number of classes (or methods) that access another class’s attributes • Violation of encapsulation

18. Metrics for Object-Oriented Testing [2/2] • Number of root classes (NOR) • The number of distinct class hierarchies • Test should be developed for each root class and corresponding hierarchy • If NOR increases, testing effort also increases • Fan-in (FIN) • Indication of multiple inheritance • FIN > 1 should be avoided • Number of children (NOC) and depth of inheritance tree (DIT)

19. Metrics for Maintenance • MT= number of modules in the current release • Fc= number of modules in the current release that have been changed • Fa= number of modules in the current release that have been added • Fd= number of modules from the preceding release that were deleted in the current release • Software maturity index SMI = MT- (Fa+ Fc + Fd) / MT • 1 indicates the product stability

20. Summary • Class-oriented metrics • Weighted methods per class, depth of the inheritance tree, number of children, coupling, response for class, lack of cohesion • Component-level design metrics • Cohesion, coupling, and complexity • Operation-oriented metrics • Average operation size, operation complexity average number of parameters per operation • Design metrics for WebApps • Metrics for source code • Metrics for object-oriented testing • Metrics for maintenance