1 Object-Oriented Metrics Renaat Verbruggen School of Computing, Dublin City University

2 Introduction and Welcome  My Background Esprit Object-oriented Project 1986 Lecturer in Software Engineering Research in formal OO Consultant to Industry on OO

3 Agenda  What is different about Object-oriented projects?  What is measurement in software development ?  What are typical OO metrics ?  What other guidelines exist?

4 Differences in OO projects 1  The code produced. Encapsulation Data Abstraction (Classes) Inheritance Polymorphism  Not just Lines of Code.

5 Differences in OO projects 2  Reuse a priority Use (reuse) of libraries or Frameworks Reuse through Inheritance Reuse above code level Patterns Business objects

6 Differences in OO projects 3  Reuse changes process Build reusable components Frameworks and libraries Abstraction, generalisation Cost  Investment Find and reuse components Saving  return on investment

7 Differences in OO projects 4  Development process iterative Often the major difference ! Growth of software over iterations Reuse-based Change considered explicitly Support for risk management  Need for early and updated metrics

8 Reasoning 1  Tom DeMarco: ''You cannot control what you cannot measure.''  Clerk Maxwell: ''To measure is to know.''

9 Reasoning 2  Lord Kelvin: ''The degree to which you can express something in numbers is the degree to which you really understand it.''  Louise Pasteur: ''A science is as mature as its measurement tools.''

10 Experience 1  Lowell Arthur: ''Better a little caution than a great regret.''  Victor Basili: ''Measurement is an excellent abstraction mechanism for learning what works and what doesn't.''

11 Experience 2  Frederick Brooks: ''Adding manpower to a late software project makes it later.''  Tom Gilb: ''Project without clear goals will not achieve their goal clearly.''

12 Experience 3  Law of Parkinson: ''Work expands to fill the available time.''

13 Measurement  What is measurement ? "Measurement is the process by which numbers or symbols are assigned to attributes of entities in the real world in such a way as to describe them according to clearly defined rules." (Norman Fenton)

14 Measurement  Units of Measurement Measure a person’s temperature  Celsius  Fahrenheit  “Feverish” - too hot (analogy) Accuracy Replicability  How do we measure software ?

15 Measurable Project Goals  Are the following measurable goals ? The software will not fail The software will be easy to use The project will be completed by June 30 The product will meet all requirements  What makes a goal measurable ?

16 Setting Measurable Goals  Metric Definition Clarity Non-ambiguous Common Understanding Replicability Accuracy ? Examples

17 Setting up Measures 1  Establish why you are measuring: Goal-Question-Metric(GQM) 1. define goal or purpose 2. Break down into questions 3. Pick suitable metrics Create a Metrics programme within company Choose metrics already developed

18 Setting up Measures 2  Create your own metrics ? Define the metric as completely as possible Define the properties of attribute to be measured Define the object to be measured, the metric’s domain Define the metric. Formality is essential

19 Setting up Measures 3  Validate the metric theoretically Prove that the properties are met Prove that the dimensions of the metric are sound Determine the scale for the metric

20 Setting up Measures 4  Validate the metric practically devise best means to measure level of automation minimum disruption for developers Use metric in several practical places. Promote metric

21 Setting up Measures 5  Example: Attribute to be measured: product size Essential property: positive, additive Metric domain: set of lines ending with ‘\n’ Metric Name:# LOC  Theory LOC is an absolute scale type Fulfils essential property of product size

22 Setting up Measures 6  Yet LOC has problems - why?  Because it is modelled simplistically  ‘\n’ s are just one element to the product size.  Used to try to capture too much about the software.

23 Software Metrics Validation  "Validation of a software measure is the process of ensuring that the measure is a proper numerical characterisation of the claimed attribute; this means showing that the representation condition is satisfied.”  Norman Fenton, City University London

24 Shyam R. Chidamber & Chris F. Kemerer  Suggested metrics for OO

25 Weighted Methods Per Class: (as sum of the McCabe numbers)  The number of methods and the complexity of methods involved is an indicator of how much time and effort is required to develop and maintain a class.  The larger the number of methods in a class the greater the potential impact on children, since children will inherit all the methods defined in the class.  Classes with large numbers of methods are likely to be more application specific, limiting the possibility of reuse.''

26 McCabe’s Cyclomatic Complexity  Based on the control graph of the program  Can be used to decide on basis path testing etc.  no. linearly independent paths = no. of edges - no of nodes + modules

27 Depth of Inheritance Tree  The deeper a class is in the hierarchy, the greater the number of methods it is likely to inherit, making it more complex to predict its behaviour.  Deeper trees constitute greater design complexity, since more methods and classes are involved.  The deeper a particular class is in the hierarchy, the greater the potential reuse of inherited methods.

28 Number of Children: (as number of immediate sub- classes)  Greater the number of children, greater the reuse, since inheritance promotes reuse.  Greater the number of children, the greater the likelihood of improper abstraction of the parent class. If a class has a large number of children, it may be a case of misuse of sub- classing.  The number of children gives an idea of the potential influence a class has on the design. If a class has a large number of children, it may require more testing of the methods in that class.

29 Response For a Class: (as number of used methods)  If a large number of methods can be invoked in response to a message, the testing and debugging of the class becomes more complicated since it requires a greater level of understanding required on the part of the tester.  The larger the number of methods that can be invoked from a class, the greater the complexity of the class.  A worst case value for possible responses will assist in appropriate allocation of testing time.

30 Coupling Between Objects 1  Excessive coupling between objects is detrimental to modular design and prevents reuse. The more independent an object is, the easier it is to reuse it in another application.  In order to improve modularity and promote encapsulation, inter-object couples should be kept to a minimum. The larger the number of couples, the higher the sensitivity to changes in other parts of the design and therefore maintenance is more difficult.

31 Coupling Between Objects 2  A measure of coupling is useful to determine how complex the testing of various parts of a design are likely to be. The higher the inter-object coupling, the more rigorous the testing needs to be.''

32 Lack of Cohesion in Methods: (disjunctive instance variables)  Cohesiveness of methods within a class is desirable, since it promotes encapsulation.  Lack of cohesion implies classes should probably be split into two or more sub/classes.  Any measure of disparateness of methods helps identify flaws in the design of classes.  Low cohesion increases complexity, thereby increasing the likelihood of errors during the development process.

33 Design Metrics And Experience 1  From Mark Lorenz  1.The average method size should be less than 8 LOC for Smalltalk and 24 LOC for C++. Bigger averages indicate O- O design problems (i.e. function-oriented coding).  2.The average number of methods per class should be less than 20. Bigger averages indicate too much responsibility in too few classes.

34 Design Metrics And Experience 2  3.The average number of instance variables per class should be less than 6. Similar in reasoning as the previous point - more instance variables indicate that one class is doing more than it should.  4.The class hierarchy nesting level should be less than 6. Start counting at the level of any framework classes that you use or the root class if you don't.  5.The number of subsystem-to-subsystem relationships should be less than the average number of class-to-class relationships within a subsystem.

35 Design Metrics And Experience 3  6.The number of class-to-class relationships within a subsystem should be relatively high.  7.The instance variable usage by the methods within a class can be used to look for possible design problems.  8.The average number of comment lines should be greater than 1. Smaller averages indicate too little documentation with the (small) methods.  9.The number of problem reports per class should be low.

36 Design Metrics And Experience 4  10.The number of times a class is reused across the original application and in other applications might indicate a need to redesign it.  11.The number of classes and methods thrown away should occur at a steady rate throughout most of the development process.

37 Other Experience 1  1.A prototype class has 10 to 15 methods, each with 5 to 10 lines of code, and takes 1 person-week to develop.  2.A production class has 20 to 30 methods, each with 10 to 20 lines of code, and takes 8 person-weeks to develop. In both these cases, development includes documentation and testing.  3.C++ will have 2 to 3 times the lines of code of Smalltalk.  4.Code volume will expand in the first half of the project and decline in the second half, as review clean up the system.

38 Other Experience 2  5.Deeply nested classes are more complex, due to inheritance.  6.A class or group of classes (e.g., a framework) with a low amount of coupling to other classes will be more reusable.  7.A class has higher cohesion if its methods utilise similar sets of instance variables.

39 Project Completion Metrics And Experience 1  1.The average number of support classes per key class... will help you to estimate the total number of classes in the final system.  2.The average man-days per application class... to estimate the amount of human resources you need to complete the project.  3.The average number of classes per developer... will help you decide what staffing level needed to develop the application.

40 Project Completion Metrics And Experience 2  4.The number of major iterations... will help you schedule times when early-release drivers can be given to customers and human factors staff to verify requirements and usability.  5.The number of subsystems should relate to major functionally-related portions of the total business' system.

41 Establishing A Metrication Programme 1  Barbara Kitchenham: 1.Define your goals (which are likely to include requirements for measurements to support basic management activities). 2.Identify measures that will support the monitoring and achievement of those goals. 3.Define and plan the metrication programme.

42 Establishing A Metrication Programme 2 4.Establish a data collection system to support the metrication programme. 5.Analyse your collected data to support your management activities and monitor achievement of your goals. 6.Review and update your goals in the light of your achievements.

43 Establishing A Metrication Programme 3  7.Update your data collection in the light of your changing goals and management requirements."

44 Software Metrics Application 1 "What do the successful companies do:  They have 'decriminalized' errors. People talk openly about what they have done wrong as a means of self-improvement. There is no need to hide failure; management is not allowed to, or simply does not, use it against you.

45 Software Metrics Application 2  Measurement is part of 'how we do business.' That is, there is no management mandate or policy that causes measurement to happen, but rather a common understanding that it is the only reasonable way to build product."

46 Software Metrics Application 3  They tend to have achieved an SEI process level of 4 or 5 (very good) without ever having passed through level 3. That is, they measure and use feedback to improve their software process without ever having invoked a defined process! (That is, of course, the epitome of technologist/experimentation vs. management/control.)

47 Object Oriented Metrics  Process of development tends to be different.  Project should not be penalised for this  Or allowed too much free rein !  Metrics are a very useful addition to an object-oriented project.

48 New Guidelines  Warning Signs  RFC > 100  RFC > 5* NMC  CBO > 5  WMC > 100  NMC > 40

49 Overall  Far more important to validate current metrics empirically than propose new ones  Aim to make link to productivity, quality and project management