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Comparing Two Learning Curves Approaches to Predict the Product Delivery Rate in a Software Factory Contract

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Abstract

In software development, the management of standardized metrics are not as frequent as it should be, which encourages the immaturity of software engineering. Currently, few companies use standards for the software functional size measurement (i.e. COSMIC); however, an increase in the adoption of this practice is emerging, derived from the need to have greater certainty, both in the estimates and in project management. A problem faced by companies that already use standardized metrics is knowing formally what proportion of improvement can be required of suppliers as they gain more experience as the time of the customer-supplier relationship passes. This article presents a comparison between the models defined by the learning curve theory, in order to determine the learning ratio of a supplier to request improvement of the productivity factor (PDR) with which the supplier has worked in previous cycles through a real case study in the Mexican industry, using the learning curve theory.

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Authors and Affiliations

  1. National Autonomous University of Mexico, Science Faculty, 04510, Mexico City, CDMX, Mexico

    F. Valdés-Souto & H. Oktaba

  2. Research Institute in Applied Mathematics and Systems, 04510, Ciudad de México, CDMX, Mexico

    D. Torres-Robledo

Authors
  1. F. Valdés-Souto
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  2. D. Torres-Robledo
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  3. H. Oktaba
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Corresponding authors

Correspondence to F. Valdés-Souto, D. Torres-Robledo or H. Oktaba.

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To analyze the productivity of a software development company, other variables must be included, such as personnel turnover and production interruptions [5], where the improvement factor is affected negatively.

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Valdés-Souto, F., Torres-Robledo, D. & Oktaba, H. Comparing Two Learning Curves Approaches to Predict the Product Delivery Rate in a Software Factory Contract. Program Comput Soft 47, 694–703 (2021). https://doi.org/10.1134/S0361768821080260

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  • DOI: https://doi.org/10.1134/S0361768821080260

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