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Recognizing Patterns for Software Development Prediction and Evaluation

  • Lionel C. Briand
  • Victor R. Basili
  • William M. Thomas

Abstract

Managing a large scale software development requires the use of quantitative models to provide insight and support control based upon historical data from similar projects. Basili introduces a paradigm of measurement based, improvement-oriented software development, called the Improvement Paradigm [1]. This paradigm provides an experimental view of the software activities with a focus on learning and improvement. This implies the need for quantitative approaches for the following uses:
  • to build models of the software process, product, and other forms of experience (e.g., effort, schedule, and reliability) for the purpose of prediction.

  • to recognize and quantify the influential factors (e.g. personnel capability, storage constraints) on various issues of interest (e.g. productivity and quality) for the purpose of understanding and monitoring the development.

  • to evaluate software products and processes from different perspectives (e.g. productivity, fault rate) by comparing them with projects with similar characteristics.

  • to understand what we can and cannot predict and control so we can monitor it more carefully.

Keywords

Software Development Measurement Vector Fault Rate Learning Sample Pattern Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    V. R. Basili and H. D. Rombach. “The TAME Project: Towards Improvement-Oriented Software Environments,” IEEE Trans. Software Eng., 14 (6), June, 1988.Google Scholar
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Copyright information

© Springer-Verlag Berlin · Heidelberg 1993

Authors and Affiliations

  • Lionel C. Briand
    • 1
  • Victor R. Basili
    • 1
  • William M. Thomas
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

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