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Object-Oriented Metrics

Object-Oriented Metrics. Renaat Verbruggen School of Computing, Dublin City University. Introduction and Welcome. My Background Esprit Object-oriented Project 1986 Lecturer in Software Engineering Research in formal OO Consultant to Industry on OO. Agenda.

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Object-Oriented Metrics

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  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

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