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Pareto Diagram

Pareto analysis helps by identifying areas that cause most of the problems, which normally means you get the best return on investment when you fix them. It is most applicable in software quality because software defects or defect density never follow a uniform distribution. Rather, almost as a rule of thumb, there are always patterns of clusterings ”defects cluster in a minor number of modules or components , a few causes account for the majority of defects, some tricky installation problems account for most of the customer complaints, and so forth. It is, therefore, not surprising to see Pareto charts in software engineering literature. For example, Daskalantonakis (1992) shows an example of Motorola's Pareto analysis for identifying major sources of requirement changes that enabled in-process corrective actions to be taken. Grady and Caswell (1986) show a Pareto analysis of software defects by category for four Hewlett-Packard software projects. The top three types (new function or different processing required, existing data need to be organized/ presented differently, and user needs additional data fields) account for more than one-third of the defects. By focusing on these prevalent defect types, determining probable causes, and instituting process improvements, Hewlett-Packard was able to achieve significant quality improvements.

Figure 5.3 shows an example of a Pareto analysis of the causes of defects for an IBM Rochester product. Interface problems (INTF) and data initialization problems (INIT) were found to be the dominant causes for defects in that product. By focusing on these two areas throughout the design, implementation, and test processes, and by conducting technical education by peer experts, significant improvement was observed . The other defect causes in the figure include complex logical problems (CPLX), translation-related national language problems (NLS), problems related to addresses (ADDR), and data definition problems (DEFN).

Figure 5.3. Pareto Analysis of Software Defects

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Another example of Pareto analysis is the problem component analysis conducted at IBM Rochester. The AS/400 software system consists of many products and components. To ensure good return on investment in quality improvement resources, a component problem index based on three indicators was calculated for each release of the software system, and for significant improvements strong focus was placed on the problem components. The problem index is a composite index of three indicators:

  • Postrelease defects from the new and changed code of the release per thousand new and changed source instructions (defects of current release origin per KCSI). If the components defect rate is

    • the same or less than the system target, then score = 0.
    • higher than system target but less than twice the system target, then score = 1.
    • higher than or equal to twice the system target but less than three times the system target, then score = 2.
    • three or more times the system target, then score = 3.
  • All postrelease defects are normalized to the total shipped source instructions of the component (all defects per KSSI). This is the defect rate for the entire component including base code from previous releases, ported code, and new and changed code. The scoring criteria are the same as above.
  • Actual number of defects categorized by quartiles. If the component is in the first quartile, then score = 0, and so forth. This indicator is from the customers' perspective because customers may not care about the lines of code for the functions and the normalized defect rates. They care about the number of defects they encounter. This indicator may not be fair to large components that will have a greater number of defects even if their defect density is the same as others. However, the purpose of the index is not for quality comparison, but to guide the improvement effort. Thus this indicator was included.

The composite component problem index ranges from 0 to 9. Components with an index of 5 and higher are considered problem components. From a Pareto analysis of a product, 27% of the components had an index of 5 and higher; they accounted for about 70% of field defects (Figure 5.4). As a result of this type of Pareto analysis, formal line items for improving problem components (e.g., component restructure, module breakup, complexity measurement and test coverage, and intramodule cleanup) were included in the development plan and have effected significant positive results.

Figure 5.4. Pareto Diagram of Defects by Component Problem Index

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Note: Figure 5.4 is not a Pareto chart in its strict sense because the frequencies are not rank ordered. For a Pareto chart, the frequencies are always in strictly descending order, and the cumulative percentage line is a piecewise convex curve. If we take a two-category view (5* + components versus others), then it is a Pareto chart.

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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