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Control estadístico de procesos en software : una revisión sistemática

Authors: Garcés, Bibiana Y.; Pino, Ricardo J.;

Control estadístico de procesos en software : una revisión sistemática

Abstract

El Mejoramiento de Procesos de Software requiere de técnicas avanzadas para el proceso de gestión cuantitativa, por lo que a partir del análisis de los indicadores pertinentes de la organización, las acciones se pueden tomar para facilitar el proceso de toma de decisiones a favor de esta mejora. Entre el conjunto de técnicas, se presta especial atención al control estadístico de procesos (SPC), que ha ganado aceptación en las empresas que se preparan para alcanzar altos grados de madurez en sus procesos, y requieren el establecimiento de programas de medición. SPC es útil en el desarrollo de software por varias razones. Gráficos de control ayudan a identificar cambios en el proceso y variaciones anormales. Por tanto, es pertinente para evaluar el éxito de SPC es en el contexto de la producción de software. En este sentido, no hemos encontrado ninguna propuesta integrada de la gestión cuantitativa aplicada al desarrollo de software distribuido, procesos que permiten la obtención de beneficios importantes, y, al mismo tiempo, que más organizaciones están mejorando sus procesos. Estas consideraciones motivan la necesidad de definir y llevar a cabo una revisión sistemática para evaluar si SPC se está utilizando con eficacia y correctamente, y para determinar los principales obstáculos para una aplicación exitosa de SPC en los esfuerzos SPI. Por lo tanto, la revisión sistemática permite el desarrollo de un análisis riguroso de la situación actual de la SPC, que es un punto de partida para abordar la forma en que se puede utilizar con éxito como una herramienta de ayuda a la decisión en la mejora de software de proceso de acuerdo con la evidencia empírica disponible reportado en la literatura.

The Software Processes Improvement requires advanced techniques for the quantitative management process, so that from the analysis of the relevant indicators in the organization, actions can be taken to facilitate the decision-making process in favor of this improvement. Among the set of techniques, special attention is devoted to the Statistical Process Control (SPC), which has gained acceptance in enterprises preparing to attain high degrees of maturity in their processes, and require the establishment of measuring programs. SPC is useful in software development for several reasons. Control Charts help identify process shifts and abnormal variations. It is therefore pertinent to assess how successful SPC is in the context of software production. In this sense, we have not found any integrated proposal of quantitative management applied to distributed software development, processes that allow the attainment of important benefits, and, at the same time, that more organizations are improving their processes. These considerations motivate the need to define and carry out a Systematic Review to assess whether SPC is being used effectively and correctly, and to determine the main obstacles to a successful application of SPC in SPI efforts. Thus, the systematic review allows developing a rigorous analysis of the current state of the SPC, which is a starting point to address how it can be successfully used as a decision-making support tool in software-process improvement in agreement with the available empirical evidence reported in the literature.

Country
Colombia
Related Organizations
Keywords

Proceso de gestión, 330, Procesos, Producción intelectual registrada - Universidad Icesi, Software, Toma de decisiones, Calidad

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green