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Statistical Software Engineering (1996) / Chapter Skim
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Summary and Conclusions
Pages 61-66

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From page 61...
... However, with the spread of software reuse, the increasing availability of tools for automatically capping requirements, generating code and test cases, and providing user documentation, and the growing reliance on standardized tuning and installation processes and standardized procedures for analysis, the mode} is moving closer to that of a traditional factory. The economy of scale that is achievable by considering software development as a manufacturing process, a factory, rather than a handcrafting process, is essential for preserving U.S.
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From page 62...
... Quality includes measuring the right things at the right time— specifically, adopted software metrics must be relevant for each of the important stages of the development life cycle, and the protocol of metrics for collecting data must be well defined and well executed. Without careful preparation that takes account of all of these data issues, it is unlikely that statistical methods will have any impact on a given software project under study.
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From page 63...
... Spiral software development process model. SSEM, statistical software engineering module.
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From page 64...
... ISSUES IN EDUCATION Enormous opportunities and many potential benefits are possible if the software engineering community learns about relevant statistical methods and if statisticians contribute to and cooperate in the education of future software engineers. The areas outlined below are those that are relevant today.
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From page 65...
... For software systems, risk analysis typically begins with identifying programming styles, characteristics of the modules responsible for most software faults, and so on. Statistical analysis of root-cause data leads to a risk profile for a system and can be useful in risk reduction.
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From page 66...
... has failed to account for; and in the presentation stage where graphics can provide succinct and convincing summaries of the statistical analysis and associated uncertainty. Visualization can also help software engineers cope with, and understand, the huge quantities of data collected in the software development process.
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Key Terms

  • software engineers
  • software development
  • statistical software
  • production process
  • exploratory data

  • process control
  • software production
  • statistical process
  • engineering module
  • statistical computing

  • hardware manufacturing
  • designed experiments
  • classical techniques
  • control techniques
  • panel expects

  • engineers cope
  • include normality
  • risk analysis
  • typically begins
  • data leads

  • related research
  • considering software
  • update information
  • clinical trials
  • data issues

  • address broad
  • user profile
  • process offered
  • computing languages
  • root cause


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