Estimating the Distribution of Total Defects over Time

Based on the discussions in the previous section, it is apparent that for software maintenance planning, we should (1) use the reliability models to estimate the total number of defects or defect rate only and (2) spread the total number of defects into arrival pattern over time based on historical patterns of field defect arrivals.

The field defect arrival patterns, in turn , can be modeled by the same process. Our experience with several operating systems indicates that the arrival curves follow the Rayleigh, the exponential, or the S models. Figure 8.5 shows the field defect arrival patterns of a major release of both the System/38 and the AS/400 operating systems. As discussed in Chapter 7, Figure 7.6 shows the field defect arrivals pattern of another IBM system software, which can be modeled by the Rayleigh curve or the Weibull distribution with the shape parameter, m, equal to 1.8.

Figure 8.5. Field Defect Arrival Pattern ”System/38 and AS/400 Operating Systems

graphics/08fig05.gif

The field defect arrival pattern may differ for different types of software. For example, in software for large systems it takes longer for latent defects to be detected and reported . The life of field defect arrivals can be longer than three years. For applications software, the arrival distribution is more concentrated and usually last about two years . We call the former the slow ramp-up pattern and the latter the fast ramp-up pattern. Based on a number of products for each category, we derived the distribution curves for both patterns, as shown in Figure 8.6. The areas under the two curves are the same, 100%. Tables 8.3 and 8.4 show the percent distribution by month for the two patterns. Because the defect arrival distribution pattern may depend on the type of software and industry segment, one should establish one's own pattern based on historical data. If the defect arrival patterns cannot be modeled by a known reliability model, we recommend using a nonparametric method (e.g., 3-point moving average) to smooth the historical data to reveal a pattern and then calculate the percent distribution over time.

Figure 8.6. Two Patterns of Field Defect Arrivals ”Areas Under the Curves Are the Same

graphics/08fig06.gif

Table 8.3. Monthly Percent Distribution of Field Defect Arrivals ”Slow Ramp-up Pattern

Month

%

Month

%

Month

%

Month

%

1

0.554

13

4.505

25

1.940

37

0.554

2

1.317

14

4.366

26

1.802

38

0.416

3

2.148

15

4.158

27

1.594

39

0.416

4

2.911

16

3.950

28

1.386

40

0.347

5

3.465

17

3.742

29

1.247

41

0.347

6

4.019

18

3.465

30

1.178

42

0.277

7

4.366

19

3.188

31

1.040

43

0.277

8

4.643

20

2.980

32

0.970

44

0.208

9

4.782

21

2.772

33

0.832

45

0.208

10

4.851

22

2.495

34

0.762

46

0.138

11

4.782

23

2.287

35

0.693

47

0.138

12

4.712

24

2.079

36

0.624

48

0.069

Year 1

42.550

Year 2

82.537

Year 3

96.605

Year 4

Cumulative

 

Cumulative

 

Cumulative

 

Cumulative

100.000

Table 8.4. Monthly Percent Distribution of Field Defect Arrivals ”Fast Ramp-up Pattern

Month

%

Month

% Month

%

1

1.592

13

5.398

25

0.277

2

3.045

14

4.706

26

0.208

3

4.429

15

4.014

27

0.128

4

5.536

16

3.391

28

0.069

5

6.505

17

2.768

29

0.069

6

7.128

18

2.215

30

0.069

7

7.474

19

1.730

8

7.612

20

1.384

9

7.474

21

1.038

10

7.197

22

0.761

11

6.713

23

0.554

12

6.090

24

0.415

Year 1

70.795

Year 2

99.169

Year 2.5

99.999

Cumulative

 

Cumulative

 

Cumulative

 

What Is Software Quality?

Software Development Process Models

Fundamentals of Measurement Theory

Software Quality Metrics Overview

Applying the Seven Basic Quality Tools in Software Development

Defect Removal Effectiveness

The Rayleigh Model

Exponential Distribution and Reliability Growth Models

Quality Management Models

In-Process Metrics for Software Testing

Complexity Metrics and Models

Metrics and Lessons Learned for Object-Oriented Projects

Availability Metrics

Measuring and Analyzing Customer Satisfaction

Conducting In-Process Quality Assessments

Conducting Software Project Assessments

Dos and Donts of Software Process Improvement

Using Function Point Metrics to Measure Software Process Improvements

Concluding Remarks

A Project Assessment Questionnaire

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Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering (2nd Edition)
ISBN: 0201729156
EAN: 2147483647
Year: 2001
Pages: 176
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