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Software as Learning: Quality Factors and LifeCycle Revised *

Summary: Software as Learning:
Quality Factors and Life­Cycle Revised *
José Hernández­Orallo and Mª José Ramírez­Quintana
Universitat Politècnica de València. Dep. de Sistemes Informàtics i Computació
Camí de Vera s/n, E­46071, València, Spain
E­mail: {jorallo, mramirez}@dsic.upv.es
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.
1 Introduction


Source: Alpuente, María - Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València


Collections: Computer Technologies and Information Sciences