Software Measurement: Lecture 1. Measures and Metrics

1. Software Measurement Software Economics 2010

2. Anton Litvinenko

Co-founder and CTO at

Metrics tracking kit for software development

3. Key competence:software measurement and metrics 9 years of software development at Programeter, Mobi, and MicroLink 4. MSc in computer science at Tartu University 5. Agenda

What is a software metric?

6. Are software metrics good or bad ? 7. What is a measure ? 8. What is a measure? Way of associating anumberwith someattribute of a physical object height -> meters temperature -> degrees Celsius 9. What is measure? One-to-one mappingbetweenphysicalobjects andformalobjects (e.g numbers) 10. Same Stuff Formally

Relational System tuple consisting of

Set of objects ( e.g. collections of apples )

11. Relations on these objects ( e.g. more, equal ) 12. Binary operations on these objects ( e.g. combine, put together )

What would be the corresponding formal relational system?

13. Relationships and Operations

Apples:

Steve Jobs has 7 apples

14. Steve Ballmer has 4 apples Jobs has more apples 15. Jobs and Ballmer can cooperate and put their apples together to have a larger pile 16. We defined a complete transition from real world into formal world 17. Same Stuff Formally... again

Let A be a relational system ofphysical objects(e.g. apples)

18. B be a relational system offormal objects( e.g.numbers) 19. m be a measure from A to B then 20. TupleA ,Bandmis ascaleif

Relations from A equivalent to relations from B

21. For each operator in A there is a corresponding operator in B 22. Why is this important? 23. What Can You Say? 24. Why is this important?

Software design:

10 modules with complexity 20 30 range

25. 20 modules with complexity 10 30 range Which one is less complex? 26. We don't have intuition for such cases 27. Intelligence Barrier 28. Example: Temperature

Facts :

Steve : today is 40 F, yesterday was 80F

29. Anton : today is 4C, yesterday was 27C Statements :

Steve :Yesterday was warmer than today

30. Anton :Yesterday was warmer than today 31. Example: Temperature

Facts :

Steve : today is 40 F, yesterday was 80F

32. Anton : today is 4C, yesterday was 27C Statements :

Steve :Yesterday was 2x times warmer

Is this ameaningfulstatement about temperature?

33. Statement ismeaningfulwhen it givessame resultonall similarscales 34. Scalesaresimilarwhen there is atransformationfrom one scale to another thatretains all defined relations and operations 35. Nominal Scale

Giving names to objects

Equality

Gender

Any naming is similar to any other

Numbers on t-shirts of football players

Any unique numbering is similar to any other

36. View from 3000 feet :) Nominal Scales Gender T-shirt Numbering 37. Ordinal Scale

Giving names inparticular order

More .... than ....

38. Middle element median

Rating of tennis players

Similar: any other rating that retains the order

39. All Ordinal Scales Are Nominal Nominal Ordinal Gender T-shirt Numbering Top 100 Grading 40. Interval Scale

Assigningnumbersso thatinterval is also meaningful

Bothmedianandarithmetic mean

41. Similar reachable viapositive linear transformation :t(x) = ax + b

Temperature in Celsius scale

Similar: Fahrenheit scale

42. Interval Scales Are Ordinal Nominal Ordinal Interval Gender T-shirt Numbering Top 100 Grading Temperature 43. Ratio Scale

Ratioof two measures ismeaningful

All statistical measures

44. Similar reachable via positive linear transformation in form oft(x) = ax Length, height, ...

Similar: Imperial units

45. Ratio Scales Are Interval Nominal Ordinal Interval Ratio Gender T-shirt Numbering Top 100 Grading Temperature Length Height 46. Absolute Scale

Only one way of measuring objects !

Similar identity transformation:t(x) = x

Counting:

My team has 5 members

47. My software is 25 lines of code 48. Absolute Scales Are Ratio Nominal Ordinal Interval Ratio Absolute Scales Gender T-shirt Numbering Top 100 Grading Temperature Length Height Team Size 49. Exercise 2

Costis usually a measure withratio scale

50. Qualityis onlyordinal(rarely interval) 51. Judgment in terms of value

Quality per unit of cost

52. Should we pay 2x for 2x quality?Combiningcost measure on aratio scalewith quality measure onordinal scale ,what scale do you get ? 53. In This Course:Metric = Measure 54. Software Metricis ameasureof anything directly related tosoftwareor itsproduction 55. Agenda

56. Examples of software metrics

Most famous :)

57. Can anybody name any software metric? 58. Lines Of Code (LOC) Product Size

59. Lines Of Code 60. Lines Of Code Summary

Accurate, easy to measure

How to interpret ...

Empty lines

61. Comments 62. Several statements on one line Language dependent 63. Doesn't respect complexity and content 64. McCabe's Cyclomatic Complexity

Thomas McCabe, 1976

65. Complexity of a program

Number of linearly independent paths through a function

66. Usually calculated using flow graph V(G) = e n + 2p

e num of edges,n num of vertices,p num of unconnected parts of graph

67. McCabe's Cyclomatic Complexity 68. McCabe's Cyclomatic Complexity

69. n = 6 70. p = 1 71. V(G) = 3 72. Cyclomatic Complexity Summary

Automated

73. Maintainability

V(G) > 10->Probability of defects rises

Testability

V(G) is an upper bound for the branch coverage

Each control structure was evaluated both to true and false

V(G) is a lower bound for the path coverage

All possible paths were executed

Doesn't respect other types of complexity

Data structure, data flow, interfaces

74. Exercise 3

CalculateLOC

75. Draw aflow graph 76. CalculateMcCabe's cyclomatic complexity Code snippet 77. Agenda

LOC and McCabe's cyclomatic complexity

79. Object oriented metrics 80. Object Oriented Metrics

Shiyam ChidamberandChris Kemerer , 1994

Metrics based on firmtheoretical basisand experience of professional software developers

81. Measure unique aspects of theobject orientedapproach 82. Inheritance Metrics

Depth of inheritance tree (DIT)

Depth of the class in the inheritance tree

Number of children (NOC)

Number of immediate descendants

NOC: 2 DIT: 2 NOC: 3 DIT: 1 83. Complexity

Weighted method count (WMC)

Sum of McCabe's cyclomatic complexities of all methods

Response for a class (RFC)

Number of public methods in a class and methods directly called by these

84. Complexity Example RFC = 6, WMC = 1 + 2 + 1 = 4 85. Coupling

Coupling between object classes (CBO)

Number of classes given class is coupled to

Lack of cohesion in methods (LCOM)

Number of method pairs that do not share instance variables vs number of methods that share at least one instance variable

86. Coupling Example CBO = 2, LCOM = 3 0 = 3 87. Coupling Example LCOM = 2 1 = 1 88. Agenda

90. Object oriented metrics 91. Object oriented design quality metrics 92. Object Oriented Design

Bad design symptoms:

Rigidity, fragility, immobility, viscosity

Class design principles

Open closed principle ,Liskov substitution principle , ...

Package architecture principles

Stable dependencies principle ,Stable abstractness principle ,

93. OO Design Quality Metrics

Robert Martin(aka Uncle Bob) , 1994

94. Measurequalityof anobject oriented design 95. Can we divide dependencies into good and bad ? Dependencies Between Classes 96. Dependencies

Stable(good) vsunstable(bad) class

97. Stable

No need to change = independent

98. Hard to change = many dependents = responsible Unstable

Depends on many = dependent

99. Easy to change = no dependents = irresponsible 100. Class Category

Class category group of highly cohesive classes

Closed and open to changes together

101. Reused together 102. Same goal

Packagesin Java,namespacesin C#

103. Dependency Metrics

Afferent Coupling (Ca) number of classes outside the category depending on the classes inside the category

Incomingdependencies

Efferent Coupling (Ce) number of classes inside the category depending on the classes outside the category

Outgoingdependencies

104. Example - Coupling Package One Package Two Package Three Ca(Package One) = 1, Ce(Package One) = 2 105. Instability (I)

Ratio ofoutgoing dependenciestototal number of dependencies

I = Ce / (Ca + Ce)

106. Stable-> I = 0 -> Ce = 0 107. Unstable-> I = 1 -> Ca = 0, Ce > 0 108. Should all categories bestable ? 109. Why astablecategory needs to beextensible ? How? 110. Abstractness (A)

Degree to which a category is abstract

Ratio ofabstract classesto thetotal number of classesin category

Completely abstract-> A = 1 -> all classes are abstract 111. Completely concrete-> A = 0 -> no abstract classes in category 112. Is there a relationship betweenInstabilityandAbstractness ? 113. Main Sequence 114. Distance From Main Sequence

D' = |A + I 1|

Normalized to range from [0, 1]

115. Agenda

117. Object oriented metrics 118. Object oriented design quality metrics 119. Developer and team metrics 120. Developer and Team Metrics

Productivity

How active developers are, how much work is being done

Knowledge

How much developers know the software they are working on

Expertise

What kind of tools and libraries developers use

Team healthiness

Communication and knowledge sharing

121. Productivity: Code Churn Metrics

Amount of code changed in the software during the period of time

122. Churned LOC number of added, modified and deleted lines of code 123. Churn Count number of changes made to a file 124. Files Churned number of changed files 125. 126. 127. 128. 129. 130. 131. 132. Code Churn Metrics

Overview ofactivityandproductivity

Increase in relative code churn metrics -> increase in defect density

Number of defects per line of code

Vulnerable files have higher code churn metrics

Vulnerability instance of violation of the security policy

133. Agenda

135. Object oriented metrics 136. Object oriented design quality metrics 137. Developer and team metrics

Productivity

138. Knowledge 139. Knowledge Metrics

Which parts of the software developer is comfortable working with?

Better planning

Does developer share her knowledge with colleagues?

Risk management

140. 141. 142. 143. 144. 145. Unique: 2 / 5-> 40% Shared: 1 / 5 -> 20% Unique: 1 / 5-> 20% Shared: 1 / 5 -> 20% 146. Example

If developer decides to leave all hisunique knowledgeislostfor the team

Unique - 35% Shared - 10% Unique - 10% Shared - 35% 147. Agenda

149. Object oriented metrics 150. Object oriented design quality metrics 151. Developer and team metrics 152. Project size metrics 153. How would you measure product size? 154. Perfect Hours

One hour ofideal engineering

How many perfect hours in a work day?

Relative measureof effort

How many ideal engineering hours required to complete the feature

Team specific 155. Applied early Manual and subjective 156. Points

Generalization of a perfect hour

Relative measureofeffortrequired to complete the feature

Not tied to time 157. Team specific 158. Applied early Manual and subjective 159. Velocity

How much work can a team complete per iteration

Completed Points Iterations 160. Function Points

Will be covered during next workshop

161. Agenda

163. Object oriented metrics 164. Object oriented design quality metrics 165. Developer and team metrics 166. Project size metrics 167. Quality metrics 168. What does high quality mean? Quality Metrics 169. Quality Metrics

Many different models , checklists

McCall's, FRUPS, ISO 9126

170. Functionality, reliability, usability, portability,

Cannot be measureddirectly -> derived from other metrics

171. 172. Quality Developer's Perspective

Comprehensibility

Style and cleanness of source code

173. Architecture and design 174. Used technologies and libraries Testability + Existing tests

Easiness of automated testing

175. Code coverage with tests 176. Quality PM's Perspective

Predictability

Effort required for development, testing, ...

177. Delivery planning 178. Additional costs Correctness

Satisfies specification

179. Serves customer needs 180. Quality Customer's Perspective

Value for money

Supports organizational goals

181. Return on investment Transparency

Partner's effort is recognizable

182. Delays and troubles are not hidden 183. Quality User's Perspective

Usability

Ease of use

184. Comprehensibility Performance

Responsive

185. Critical functionality is quick Functionality

Software does the right thing

186. Example: Defect Detection Percentage

Efficiency of quality assurance procedures

How manybugswere delivered to customer

DDP = E / (E + D)

E errors foundbefore delivery

187. D defects = errors foundafter delivery

What would be an ideal situation?

188. Example: Time Between Escaped Defects

How often new defects are found indelivered versionsof the product

How would you use this metric?

189. Course:IDY0204 Software Quality and Standards 190. Agenda

191. Examples of software metrics 192. Classification of software metrics 193. Classification of Software Metrics Subject of measurement 194. Subject:Development Process

Measuring the efficiency of process application

195. On the organizational level strategic purposes 196. On the project level tactical purposes

Examples of metrics

Length of (development) iteration

197. Number of changes in requirements 198. Number of finished tasks 199. Defect detection percentage 200. Subject:Resources

Measuring usage of personnel & resources and their properties

Examples of metrics

Developer competency

201. Developer fluctuation 202. Developer productivity and know-how in the project 203. Maturity of the code written by developer 204. Subject:Product

Measuring product attributes

Size, complexity, scalability

Examples of metrics

LOC, commented lines of code, function points

205. McCabe's cyclomatic complexity 206. Code coverage with test 207. Code stability 208. Classification Overview What is measured? 209. Classification of Software Metrics Lines of Code vs Quality 210. DirectMetrics

Directly measurable

Examples of metrics:

LOC, function points

211. McCabe's cyclomatic complexity 212. Number of requirements 213. IndirectMetrics

Not possible to measure directly

Derived from other properties

Examples of metrics

Code quality, code readability

214. Developer productivity, efficiency 215. Reliability 216. Classification Overview What is measured? Is it measurable? 217. Classification of Software Metrics (In)dependency on the measurement context 218. InternalAttributes

Measurement context/environment is not relevant

Examples of metrics

219. McCabe's cyclomatic complexity 220. Code coverage with tests 221. ExternalMetrics

Measured with respect to environment/context

Examples of metrics

Software reliability

222. Developer productivity 223. Source code comprehensibility 224. Usability 225. Classification Overview What is measured? Is it measurable? Is context dependent? 226. Classification Example What is measured? Is it measurable? Is context dependent? 227. References

G. Ford, Measurement theory for software engineers

http://courses.cs.ut.ee/2010/se/uploads/Main/measurement-theory.pdf

Wikipedia

http://en.wikipedia.org/wiki/Software_metrics

C. Lange, Metrics in software architecting

http://www.win.tue.nl/~mchaudro/sa2007/Metrics%20Architecting%202005.pdf

M. Gkmen, Software process and project metrics

http://www3.itu.edu.tr/~gokmen/SE-lecture-2.pdf

H. Nestra, Metrics, Software engineering 2005

http://courses.cs.ut.ee/2005/tvt/uploads/Main/software_engineering_21.pdf

Lines of code

http://en.wikipedia.org/wiki/Source_lines_of_code

228. References II

McCabe's cyclomatic complexity

http://en.wikipedia.org/wiki/Cyclomatic_complexity

229. http://www.stsc.hill.af.mil/crosstalk/1994/12/xt94d12b.asp 230. http://www.answers.com/topic/cyclomatic-complexity S. Chidamber and C. Kemerer, A metrics suite for object oriented des.

http://bit.ly/2xY21F

C. Martin, OO Quality design metrics

http://cs.allegheny.edu/wiki/cs290F2004/uploads/123/oodmetrc.pdf

R. Pressman, Software engineering: a practitioner's approach

http://bit.ly/gCWvm

http://www.laynetworks.com/Software%20Engineering.htm

231. http://www.parlezuml.com/metrics/OO%20Design%20Principles%20&%20Metrics.pdf 232. http://www.parlezuml.com/metrics/Metrics%20Definitions.pdf 233. Home Reading David Longstreet Function Point Manual 234. Thank you for your time and attention!

Software Measurement: Lecture 1. Measures and Metrics

Technology

Transcript of Software Measurement: Lecture 1. Measures and Metrics