Workshop 1: Software Measurement. Marlon Dumas

Transcription

1 Software Economics Fall 2013 Workshop 1: Software Measurement Marlon Dumas (based on slides by Anton Litvinenko)

2 Main message Software measures can be misleading, so Either you don t use them Or you better know what they mean and how to use them.

3 What is a measure? Way of associating a formal object (e.g. number) to an attribute of a physical object height temperature 3

4 Relationships and Operations Apples: - Steve Jobs has 7 apples - Steve Ballmer has 4 apples Jobs has more apples Jobs and Ballmer can cooperate and put their apples together to have a larger pile 4

5 Scales Let A be a relational system of physical objects (e.g. apples) B be a relational system of formal objects (e.g. numbers) m be a measure from A to B then Tuple (A, B, m) is a scale if - Relations from A equivalent to relations from B - For each operator in A there is a corresponding operator in B 5

6 What Can You Say? 6

7 What can we say? Software design: - 10 modules with complexity range - 20 modules with complexity range Which one is less complex? Lesson: Not all scales are additive. 7

8 Example: Temperature Facts: - Steve: today is 40ºF, yesterday was 80ºF - John: today is 4ºC, yesterday was 27ºC Statements: - Steve: Yesterday was warmer than today - John: Yesterday was warmer than today 8

9 Example: Temperature Facts: - Steve: today is 40ºF, yesterday was 80ºF - John: today is 4ºC, yesterday was 27ºC Statements: - Steve: Yesterday was 2x times warmer Lesson: not all scales are multiplicative 9

10 Nominal Scale Giving names to objects - Equality Numbers on t-shirts of football players Numerical object identifiers (OID) - Any unique numbering is similar to any other 10

11 Ordinal Scale Giving names in particular order - More... than... - Middle element median Rating of tennis players - Similar: any other rating that retains the order 11

12 All Ordinal Scales Are Nominal T-shirt Numbering Nominal OID Ordinal Grading Top

13 Interval Scale Assigning numbers so that interval is also meaningful - Both median and arithmetic mean - Similar reachable via positive linear transformation: t(x) = ax + b Temperature in Celsius scale - Similar: Fahrenheit scale 13

14 Interval Scales Are Ordinal T-shirt Numbering Grading Nominal Ordinal OID Top 100 Interval Temperature 14

15 Ratio Scale Ratio of two measures is meaningful - All statistical measures - Similar reachable via positive linear transformation in form of t(x) = ax Length, height,... - Similar: Imperial units 15

16 Ratio Scales Are Interval T-shirt Numbering Grading Temperature Nominal Ordinal Interval Gender Top 100 Ratio Length Height 16

17 Absolute Scale Based on counting the objects or basic elements. - Similar identity transformation: t(x) = x Counting: - My team has 5 members - My software is 25 lines of code 17

18 Absolute Scales Are Ratio T-shirt Numbering Grading Temperature Length Nominal Ordinal Interval Ratio Absolute Scales Team Size Gender Top 100 Height 18

19 Exercise 1 Suggest a measure and a scale for - Processing capacity of a computer - Size of a software system - Effort required to develop a software system - User-friendliness of a software system - Quality of a software system 19

20 Software Measure is a measure of anything directly related to software or its production Often, Software Metric is used as synonym for Software Measure, although metric has a specific meaning in Maths (e.g. triangle inequality) 20

21 Agenda 1. What is a software metric? 2. Examples of software metrics 1. The first that comes to one s mind :) 21

22 Lines Of Code (LOC) Product Size

23 Lines Of Code 23

24 Lines Of Code Summary Accurate, easy to measure How to interpret... - Empty lines - Comments - Several statements on one line Language dependent Doesn't take into account complexity Useful? 24

25 McCabe's Cyclomatic Complexity Thomas McCabe, 1976 Complexity of a program - Number of linearly independent paths through a function - Usually calculated using the flow graph V(G) = e n + 2p - e num of edges, n num of vertices, p num of unconnected parts of graph 25

26 McCabe's Cyclomatic Complexity 26

27 McCabe's Cyclomatic Complexity 2: System.out.println(" "); for (Client c : clients) 4-5: System.out.println(c.getId() + " " + c.getfirstname()); e = 7 n = 6 p = 1 V(G) = 3 if (clients.size() == 0) 8: System.out.println("\tNothing"); 10: System.out.println(" "); 27

28 Cyclomatic Complexity Summary Automated (available in any modern IDE) Related to testing notions - 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 Related to maintainability and defects - V(G) > 10 Probability of defects rises - But to be used with care: N. Nagappan, T. Ball, A. Zeller, Mining metrics to Predict Component Failures. ICSE'

29 Exercise 2 Calculate McCabe's cyclomatic complexity of the following code snippet: 29

30 Agenda 1. What is a software metric? 2. Examples of software metrics 1. LOC and McCabe's cyclomatic complexity 2. Object oriented design metrics 30

31 Coupling Coupling between object classes (CBO) - Number of classes a given class is coupled to Lack of cohesion in methods (LCOM) - Number of method pairs that do not share instance variables minus number of methods that share at least one instance variable - By convention, LCOM is said to be zero if the above definition gives a negative number 31

32 Coupling Example CBO = 1 (+ 1 library), LCOM =? 32

33 Coupling Example LCOM = 2 1 = 1 33

34 Agenda 1. What is a software metric? 2. Examples of software metrics 1. LOC and McCabe's cyclomatic complexity 2. Object oriented design metrics 34

35 Agenda 1. What is a software metric? 2. Examples of software metrics 1. LOC and McCabe's cyclomatic complexity 2. Object oriented metrics 3. Developer/team metrics 35

36 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 - etc. 36

37 Productivity: Code Churn Metrics Amount of code changed in the software during the period of time - Churned LOC number of added, modified and deleted lines of code - Churn Count number of changes made to a file - Files Churned number of changed files 37

38 Applications of Code Churn Metrics Overview of activity and productivity 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 Question: Is Productivity additive? Multiplicative? (Hint: The Mythical Man Month) 38

39 Agenda 1. What is a software metric? 2. Examples of software metrics 1. LOC and McCabe's cyclomatic complexity 2. Object oriented metrics 3. Developer and team metrics 4. Project size metrics 39

40 Perfect Hours One hour of ideal engineering - How many perfect hours in a work day? Relative measure of effort - How many ideal engineering hours required to complete the feature Team specific Applied early Manual and subjective 40

41 Story Points Generalization of a perfect hour - Relative measure of effort required to complete the feature Not tied to time Team specific Applied early Manual and subjective 41

42 Function Points Will be covered during next workshop 42

43 Agenda 1. What is a software metric? 2. Examples of software metrics 1. LOC and McCabe's cyclomatic complexity 2. Object oriented metrics 3. Object oriented design quality metrics 4. Developer and team metrics 5. Project size metrics 6. Quality metrics 43

44 Quality Metrics Many different models, checklists - McCall's Quality Model, FRUPS, ISO Functionality, reliability, usability, portability, Cannot be measured directly must be measured via other metrics (indirect metrics) 44

45 Example: Defect Efficiency Ratio Efficiency of quality assurance procedures - How many bugs were delivered to customer DER = E / (E + D) - E errors found before delivery - D defects = errors found after delivery What would be an ideal situation? 45

46 Exercise 3 We have a tight schedule and we asked two testers Alice and Bob to test 5 software components Bob reported that he had time to thoroughly test only 1st component and have found totally 20 bugs Alice reported that she partially tested all components and found 5, 6, 4, 2 and 5 bugs correspondingly Whose report is more useful? What decisions can you make? 46

47 Question How do you think where is the highest probability of finding undetected bug? Why? - In components showing LARGE number of known bugs - In components showing SMALL number of known bugs 47

48 Observation "as the number of detected errors in a piece of software increases the probability of the existing of more undetected errors also increases" Glenford Myers, 1976 Bugs tend to cluster as you find a bug you should stop and write more tests for components where you have found a bug 48

49 Home Reading David Longstreet Function Point Manual 49

50 References Some material inspired by or extracted from: G. Ford, Measurement theory for software engineers C. Lange, Metrics in software architecting M. Gökmen, Software process and project metrics C. Martin, OO Quality design metrics 123/oodmetrc.pdf