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Software as Learning: Quality Factors and Life-Cycle Revised

  • José Hernández-Orallo
  • Ma José Ramírez-Quintana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1783)

Abstract

In this paper Software Development (SD) is understood explicitly as a learning process, which relies much more on induction than deduction, with the main goal of being predictive to requirements evolution. Concretely, classical processes from philosophy of science and machine learning such as hypothesis generation, refinement, confirmation and revision have their counterpart in requirement engineering, program construction, validation and modification in SD, respectively. Consequently, we have investigated the appropriateness for software modelling of the most important paradigms of modelling selection in machine learning. Under the notion of incremental learning, we introduce a new factor, predictiveness, as the ability to foresee future changes in the specification, thereby reducing the number of revisions. As a result, other quality factors are revised. Finally, a predictive software life cycle is outlined as an incremental learning session, which may or may not be automated.

Keywords

Quality Factor Software Engineering Logic Program Scientific Theory Incremental Learning 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • José Hernández-Orallo
    • 1
  • Ma José Ramírez-Quintana
    • 1
  1. 1.Dep. de Sistemes Informàtics i ComputacióUniversitat Politècnica de ValènciaValènciaSpain

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