1. Pop Quiz Define Fan-in and Fan-out
Does it matter if (what should be done about) code (which) has a high Fan-in and a high Fan-out content?
What is the goal of a Software Quality Management Model?
Why is the Rayleigh model good for quality management?

2. Software Quality Engineering CS410
Class 11
Complexity Metrics and Models

3. Complexity Metrics and Models
Provide clues to help focus quality improvement efforts by looking at the program-module level.
Focused on internal dynamics of design and code
Studied by Computer Scientists and Software engineers
In contrast, Reliability and Quality Management Models are:
Unit of analysis is not as granular
Focused on external behavior of process (eg. inspection defects), or product (eg. failure data).
Studied by researchers, reliability experts, and project managers

4. Lines of Code (LOC) A representation of program size
Can be measured in different ways (chap. 4)
HLL Source Statements
Assembler Statements
Executable lines
Executable lines plus data definitions

5. Lines of Code (LOC) General assumption:
The more lines of code, the more defects are expected, and a higher defect density (defects per KLOC) is expected
Research has shown that the general assumption may not be true in all cases.
A optimum code size may exist in which expected defect rates would be contained within an acceptable upper limit.
Such an optimum may depend on language, project, product, and environment

6. Lines of Code (LOC)
A curvilinear (p ) relationship more accurately describes the relationship between code size and defect density:
Example: Fig p. 255
Module size affects defect density:
Small modules require more external interfaces
Large modules become very complex
Key is to use empirical data, historical data, and comparative data to establish a guideline for module size

7. Halstead’s Software Science
Halstead (1977) stated that Software Science is different from computer Science and that Software Science consists of programming tasks.
A programming task is the act of selecting and arranging a finite number of program ‘tokens’ which are basic syntactic units distinguishable by a compiler.

8. Halstead’s Software Science
Tokens can be classified as either operators or operands.
Primary measures for Halstead’s Software Science are:
n1 = the number of distinct operators that appear in a program
n2 = the number of distinct operands that appear in a program
N1 = the total number of operator occurrences
N2 = the total number of operand occurrences

9. Halstead’s Software Science
Based on the four primitives a system of equations describing the program can be applied:
Total Vocabulary
Vocabulary (n) = n1 + n2
Overall Program Length
Length (N) = N1 + N2
Potential Minimum Volume (in bits) for algorithm
Volume (V) = NLog2(n1 + n2)

10. Halstead’s Software Science
Program Level (a complexity measure)
Level (L) = n1 + n2
Program Difficulty
Difficulty (D) = (n1/2) x (N2/n2)
Development Effort
Effort = V / L
Projected number of faults
Faults (B) = V / S
where S equals a mean number of decisions between errors (3000 used as default)

11. Halstead’s Software Science

12. Halstead’s Software Science
Problems with Halstead’s Software Science:
Faults equation is oversimplified. It simply states that the number of faults in a program is a function of its volume.
Equations do not provide relevant information, only good for comparison
Data for predictions must be available to perform equations, which means code must already be written.

13. Cyclomatic Complexity
McCabe (1976) designed a system to indicate a programs testability and understandability (maintainability).
A.K.A. McCabes Complexity Index or CPX
Based on graph theory of cyclomatic number of regions in a (flow) graph
Represents the number of linearly independent paths comprising a program
Gives an upper bound to the number of test-cases that would be required for path testing

14. Cyclomatic Complexity
General formula:
V(G) = e - n + 2p
where:
e = number of edges
n = number of nodes
p = number of unconnected parts to the graph or
V(G) = e - n + 2
If all parts are connected

15. Cyclomatic Complexity
Example:
set of processing a;
If cond1 Then
set of processing b
else
set of processing c;
set of processing d;
If cond2 then
set of processing e;
set of processing f;
set of processing g;

16. Cyclomatic Complexity
Example:
More complexity

17. Cyclomatic Complexity
Cyclomatic Complexity also can be computed based on the decision in a program:
n binary decisions: V(g) = n + 1
three-way decision: counts as 2n + 1 binary decisions
n-way decision: counts as n - 1 binary decisions
loops: count as 1 binary decision
note: does not distinguish between different type of control flow (eg. loops vs. IF-THEN-ELSE)
Cyclomatic complexities are additive
The Cyclomatic Complexity of one large graph is the sum of the individual graph’s complexities.

18. Cyclomatic Complexity
McCabes recommendation:
To achieve a good testability and maintainability, no program module should have a Cyclomatic Complexity greater than 10.
Cyclomatic complexity correlates strongly with program size (LOC).
There also tends to be a positive correlation between Cyclomatic Complexity and defects.
Cyclomatic Complexity appeals to many software developers because it is tied to decisions and branches (logic).

19. Syntactic Constructs
Studies that look at syntactic makeup of a program.
Shen (1985)
Found a correlation between the number of unique operands and the presence of defects
Lo (1992)
Found correlation between field defects in modules, and LOC, IF-THEN-ELSE’s, DO-WHILE’s, unique operands, and number of calls
DO-WHILE turned out to be the greatest factor, and a positive correlation between DO-WHILE use and defects was discovered (programmers needed training)

20. Structure Metrics
Previous metrics all assume that each module is a independent entity.
Structure metrics take into account that (significant) interactions between modules exist.
Yourdon/Constantine (1979) & Myers (1978) both proposed fan-in and fan-out metrics based on the idea of coupling.

21. Structure Metrics Fan-out = number of modules called
Fan-in = number of modules called by
Module Coupling = “connectedness”
Other Factors:
Inputs, Outputs, and Global Variables
Low Module Coupling = relatively low number of inputs, outputs, and calls
High Coupling = relatively high number of inputs, outputs, and calls

22. Structure Metrics
Small/Simple modules are expected to have high fan-in. These modules are usually located at the lowest levels of the system structure.
Large/Complex modules are expected to have low fan-in.
Modules should generally not have both high fan-in and high fan-out. If so - then module is good candidate for redesign (functional decomposition).

23. Structure Metrics
Modules with high fan-in are expected to have low defect levels.
Fan-in is expected to have negative or insignificant correlation with defects
Modules with high fan-out are expected to have higher defect levels.
Fan-out is expected to have positive correlation with defects

24. Structure Metrics Card and Glass (1990) System Complexity Model:
System Complexity is equal to the sum of structural complexity plus data complexity
where:
structural complexity is equal to mean of squared values of fan-out (fan-in is insignificant)
data complexity is average I/O variables

25. Complexity Metrics and Models Criteria for Evaluation
Explanatory Powers - the metrics/model ability to explain the interrelationships among complexity, quality, and other programming and design parameters.
Applicability - the degree to which the metrics/models can be applied by software engineers to improve the quality of design, code and test.

26. Complexity Summary
Complexity metrics and models describe the complexity characteristics of software components (modules).
Software complexity traditionally has a positive correlation to defects.
A key to achieving good quality software is to reduce the complexity of software designs and implementations.

27. Pop Quiz What are metrics and models for?
List some advantages of Halstead’s Software Science (you may want some disadvantages, too…)
What is the Back End?
So what’s the Front End?
How good is “good enough”?

28. Software Quality Engineering CS410
Class 11b
Measuring and Analyzing
Customer Satisfaction

29. Customer Satisfaction
Customer satisfaction is the ultimate validation of quality!
Product quality and customer satisfaction together form the total meaning of quality.
TQM links customer satisfaction to product quality, and focuses on long-term business success.
Customer Focus - achieve high Customer Satisfaction
Process - reduce process variation and achieve continuous process improvement
Human side of Quality - quality culture
Measurement and Analysis - goal-oriented measurement

30. Customer Satisfaction
Customer satisfaction is important because:
Enhancing customer satisfaction is the bottom line of business success
Retaining existing customers is becoming tougher with ever-increasing market competition
Studies show that it is five times more costly to recruit a new customer than it is to retain an old customer
Dissatisfied customers tell 17 to 20 people about their (negative) experiences
Satisfied customers tell 3 to 5 people about their (positive) experiences

31. Customer Satisfaction Surveys
Three type of Survey:
Face-to-Face
Telephone Interviews
Mailed Questionnaires
Face-to-face interviews
interviewer asks questions from pre-structured questionnaire and records the answers
advantage - high degree of validity of the data because the interviewer can note specific reactions and eliminate misunderstandings about questions being asked
disadvantages - cost, interviewer bias, recording errors, training

32. Customer Satisfaction Surveys
Telephone Interviews
Similar to face-to-face interviews
interviewer asks questions from pre-structured questionnaire and records the answers over the phone
advantage - interviews can be monitored to ensure consistency and accuracy
advantage - less expensive than face-to-face
advantage - can be automated (computer-aided)
disadvantage - lack of direct observations, and lack of contacts (due to not scheduling a meeting)

33. Customer Satisfaction Surveys
Mailed Questionnaire
Does not require interviewers and is therefore less expensive
disadvantage - lower response rates, and samples may not be representative of the population (skewed results - only the people who really have something to say may respond)
Questionnaire must be carefully constructed, validated and pre-tested before being used.

34. Customer Satisfaction Surveys
Comparison of three survey methods:

35. Customer Satisfaction Surveys
Sampling methods:
When customer base is very large it is not possible (or feasible) to sample every customer
Customer satisfaction must be estimated based on a sub-set of population
Four types of probability sampling:
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling

36. Customer Satisfaction Surveys
Simple random sampling
Every sample of size n has the same chance of being selected from the population
Method
List each individual is listed once (and only once)
Some mechanical (I.e. a random number generator) process is used to draw the sample
On each draw the already selected individuals are removed from the list
Probabilities of being selected are always equal for each of the remaining individuals

37. Customer Satisfaction Surveys
Systematic sampling
Similar to simple random except that a ratio of samples is selected and a random number is only used for the starting point
Method
Determine population (p)
Determine desired sample size (s)
Determine sampling ratio (k = p/s)
Determine the sampling fraction (f = 1/k)
Randomly select starting point between 1 and k
Example:

38. Customer Satisfaction Surveys
Stratified sampling
Individuals are classified into non-overlapping groups called strata and then simple random samples are selected from each stratum.
Strata selection is usually based on some aspects of the customer environment.
Cluster Sampling
Individuals are group into many clusters (groups) based on some characteristic and then a cluster is selected and sampled.

39. Customer Satisfaction Surveys
Sampling considerations:
Simple random sampling is generally the least expensive sampling method.
Systematic sampling can be biased if:
The list is ordered
Cyclical characteristics conflict with k
Stratified Sampling is usually more efficient, and yields greater accuracy than simple sampling (individuals in each stratum are represented).
Cluster sampling is generally less efficient, but also less expensive than stratified sampling.

40. Customer Satisfaction Surveys
Sample size:
How large should sample size be?
Dependant on:
Confidence level desired
Margin of error that can be tolerated
Larger sample sizes give higher confidence levels and lower margins of error.
Power of sample is dependant on absolute size rather than percentage of population.

41. Customer Satisfaction Surveys
Analyzing Satisfaction Data:
Five-point (Likert scale) is the most commonly used measure.
Run charts can be used to show graphical representation of survey results.
Note - margin of error can also be included.
Example - fig p. 280
Data can be viewed as:
percent satisfied: - where are we doing well
percent unsatisfied (neutral, dissatisfied, and very dissatisfied) - where do we need to focus

42. Customer Satisfaction Surveys
Specific Attributes:
Measuring specific attributes of the software helps to provide data which will indicate areas of improvement.
The profile of customer satisfaction with those attributes indicates the areas of strength and weakness of the software product.
Notes:
Must be careful with correlating weakness with priority, it may not always be the desirable case.
More important is correlation of specific attribute to overall satisfaction level.

43. Customer Satisfaction Surveys Specific Attributes Examples:
IBM - CUPRIMDSO
Capability
Usability
Performance
Reliability
Installability
Maintainability
Documentation
Service
Overall
Hewlett-Packard - FURPS
Functionality
Usability
Reliability
Performance
Serviceability

44. Customer Satisfaction Surveys
Overall Satisfaction
Each attribute has some correlation to the overall satisfaction rating.
Determining the correlation is the key to prioritizing improvement plans.
Methods for determining correlation (pp ):
Least squares multiple regression
Logistic regression

45. Customer Satisfaction Surveys
Method for determining priorities:
1. Determine the order of significance of each quality attribute on overall satisfaction by statistical modeling.
2. Plot the coefficient of each attribute from the model (Y-axis) against its satisfaction level (X-axis).
3. Use the plot to determine priority by:
Top to Bottom
Left to right if same coefficients
Example: Fig p. 287

46. Customer Satisfaction Surveys
Satisfaction with the company:
Adds a broader view to Customer Satisfaction
Overall satisfaction and loyalty is attributed to a set of common attributes of the company:
Ease of doing business
Partnership
Responsiveness
Knowledge of the customer’s business
Customer driven

47. Customer Satisfaction Surveys
Key dimensions of company satisfaction:
Technical solutions: quality.reliability, availability, ease of use, pricing, installation, new technology, etc.
Support and Service: flexible, accessible, product knowledge, etc.
Marketing: solution, central point of contact, information, etc.
Administration: purchasing procedure, billing procedure, warranty expiration notification, etc

48. Customer Satisfaction Surveys
Key dimensions of company satisfaction:
Delivery: on time, accurate, post-delivery process, etc.
Company image: technology leader, financial stability, executives images, etc.
Perceived performance is equally as important as actual performance.
Company level data is an important aspect of overall customer satisfaction.

49. Customer Satisfaction Surveys
How good is good enough?
Is it worth it to spend $x to gain y amount of satisfaction?
The key to this question lies in the relationship between customer satisfaction and market share.
Basic assumption - Satisfied customers continue to purchase products from the same company and dissatisfied customers will purchase from other companies.

50. Customer Satisfaction Surveys
As long as market competition exists, then customer satisfaction will be the key to customer loyalty.
Even in a monopoly, customer dissatisfaction will encourage the development and emergence of competition.
Answer to “How good is good enough”
Better than the competition.

51. Customer Satisfaction Surveys
Customer Satisfaction Management Process should cover:
1. Measure and monitor overall CS of own company and competition over time.
2. Analyze specific satisfaction dimensions, attributes, strengths, weaknesses, and priorities
3. Perform root cause analysis to identify inhibitors of each dimension and attribute
4. Set satisfaction targets (overall and specific), taking competition into consideration
5. Formulate and implement action plans based on results of 1-4.

52. Customer Satisfaction Summary
Customer Satisfaction at both the product level and company level must be analyzed and managed.
Company data is valuable for a comprehensive approach to Total Quality Management (TQM)
Product data is valuable for identifying specific areas for product improvements
Challenge - knowing why customer’s chose a competitor. CS surveys only target customers who have chosen your company. May need independent consulting data for this.

53. Pop Quiz
Name several kinds of Customer Satisfaction survey

54. Software Quality Engineering CS410
Class 11c
AS/400 Software Quality Management

55. AS/400 What is it? Who uses it? Application System/400 (AS/400)
A midrange based multi-user, multi-programming operating system from IBM Rochester, MN
Who uses it?
Small, medium, and large Information Technology (IT) companies, as well as universities, and research organizations.
Customer base is estimated at 250,000+

56. AS/400
How big is it?
Initial release (August 1988) 7.1 million lines of code (7,000 KLOC)
Approx. 10 new releases since GA (general availability)
Typical release has 2 million lines of new/changed code
Estimated size of current product 21.1 million (21,000 KLOC) lines of code (assuming a 70/30 split of new/changed code per release, and one release per year since GA)

57. AS/400
Track Record
Market Driven Quality (MDQ - a system based on the TQM management style and corporate culture) implemented in 1990
Benchmark studies against Motorola, IBM Houston (NASA Shuttle software), and other recognized leaders
Received Malcolm Baldrige National Quality Award
ISO 9000 Certification

58. AS/400 Key AS/400 quality strategy: Defect Prevention Process (DPP)
Focus on Design Review/Code Inspection (DR/CI)
Component test improvements
Departmental 5-UP measurements (key metrics)
Customer Satisfaction
Post-release defect rate
Customer problem calls
Fix Response time
Number of defective fixes
Quality Recognition
Management commitment
Quality culture

59. AS/400 AS/400 Software Quality Management System (SQMS)
Goal - to reduce the product defects and increase the customer satisfaction
Five key elements:
People
In-process quality management
Continuous process improvement
post-GA product quality management
Customer satisfaction management

60. AS/400 People In-process product quality management
Most important element of SQMS
Must be highly motivated to meet quality goals
Must be talented (and trained) to execute and improve the development process
In-process product quality management
Measure the results of various process steps
Determine if the product is on target for achieving the product quality goals
Help define action plans for quality improvements

61. AS/400 Continuous process improvement
Provides a foundation for improving process
Triggered by customer reported defects, and DPP
Supported by tools and technology that promote process automation, increase defect discovery, or reduce defect injection
Post-GA product quality management
Addresses customer problems (both defect oriented and non defect oriented)

62. AS/400 Post-GA product quality mgmt (cont.) Goals Fix problems quickly
Fix problems with high quality
Learn from errors that were made
Gather valuable metrics
Feed next release requirements phase
Suggest process improvements

63. AS/400 Customer satisfaction management
Understand overall customer satisfaction
Analyze customer satisfaction in regard to different parameters of the product
Define product requirements
Help prioritize problems based on customer satisfaction
Monitor critical situations and help to minimize impacts to customer’s business

64. AS/400 Summary “There are no silver bullets”
Many quality improvement techniques and solutions exist, but the key is placing them into practice systematically and persistently.
Lack of functional defects is the most basic measure of quality, however Customer Satisfaction represents the final evaluation of the product.