Applicability of the software cost model COCOMO II to HPC projects

Miller, Julian; Wienke, Sandra; Schlottke-Lakemper, Michael; Meinke, Matthias; Müller, Matthias S.

doi:10.1504/ijcse.2018.095849

Cited by 7 publications

(5 citation statements)

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“…Closely related to the Function Point method, the algorithmic cost model Constructive Cost Model (COCOMO) is also used in software development for cost or effort estimation. Miller et al (2018) demonstrate the applicability of the COCOMO in practice. Singh et al (2018) estimate the effort and costs of software development from the manufacturer's perspective based on a parametric algorithm using historical information from past projects.…”

Section: Review Resultsmentioning

confidence: 83%

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Valuation and pricing of software licenses to support supplier–buyer negotiations: A case study in the automotive industry

2021

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Licensed standard software can be replicated millions of times at near zero cost for the supplier and poses immense challenges for purchasing and cost managers. A model is introduced to evaluate the appropriateness of software prices and thus to support the negotiation and decision process regarding suppliers. The model determines the “value to customer” of a software product using conjoint analysis and converts that value into a theoretical license price. In a case study coming from a large Original Equipment Manufacturer (OEM) it is shown that the developed conjoint model can successfully be used in practice and suggests realistic purchase prices.

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Section: Review Resultsmentioning

confidence: 83%

Section: Literature Reviewmentioning

confidence: 99%

Valuation and pricing of software licenses to support supplier–buyer negotiations: A case study in the automotive industry

2021

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“…The collected productivity data of the students opens up wide areas of research into HPC programming productivity such as the estimation of software costs of HPC projects. While most of this research is ongoing and will require more data, some early results can be found in [2,3,6]. Figure 2 provides an example for the analyses carried out on the productivity data collected during the OpenMP lab in summer 2019.…”

Section: Evaluation Of the Labsmentioning

confidence: 99%

Self-paced Learning in HPC Lab Courses

Terboven¹,

Miller²,

Wienke³

et al. 2020

JOCSE

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In a software lab, groups of students develop parallel code using modern tools, document the results and present their solutions. The learning objectives include the foundations of High-Performance Computing (HPC), such as the understanding of modern architectures, the development of parallel programming skills, and coursespecic topics, like accelerator programming or cluster setup. In order to execute the labs successfully with limited personnel resources and still provide students with access to world-class HPC architectures, we developed a set of concepts to motivate students and to track their progress. This includes the learning status survey and the developer diary, which are presented in this work. We also report on our experiences with using innovative teaching concepts to incentivize students to optimize their codes, such as using competition among the groups. Our concepts enable us to track the eectiveness of our labs and to steer them for increasing sizes of diverse students. We conclude that software labs are eective in adding practical experiences to HPC education. Our approach to hand out open tasks and to leave creative freedom in implementing the solutions enables the students to self-pace their learning process and to vary their investment of eort during the semester. Our eort and progress tracking ensures the achieving of the extensive learning objectives and enables our research on HPC programming productivity.

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“…Além disso, encontrar uma amostra representativa de participantes experientes no domínio da programac ¸ão paralela é desafiador. Logo, o uso de métricas de codificac ¸ão bem estabelecidas na área de Engenharia de Software (e.g., Linhas de Código (LOC), Halstead e Constructive Cost Model -COCOMO) pode facilitar a avaliac ¸ão da produtividade [Adornes et al 2015, Griebler et al 2018, Miller et al 2018, Rodriguez-Canal et al 2021, Martínez et al 2022, Peccerillo e Bartolini 2022. Porém, nosso estudo anterior [Andrade et al 2021] mostrou limitac ¸ões, uma vez que estas métricas de codificac ¸ão não foram projetados para avaliar aplicac ¸ões paralelas.…”

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Opinião de Brasileiros Sobre a Produtividade no Desenvolvimento de Aplicações Paralelas

Andrade

Griebler²,

Santos³

et al. 2022

Anais Do XXIII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2022)

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A partir da popularização das arquiteturas paralelas, surgiram várias interfaces de programação a fim de facilitar a exploração de tais arquiteturas e aumentar a produtividade dos desenvolvedores. Entretanto, desenvolver aplicações paralelas ainda é uma tarefa complexa para desenvolvedores com pouca experiência. Neste trabalho, realizamos uma pesquisa para descobrir a opinião de desenvolvedores de aplicações paralelas sobre os fatores que impedem a produtividade. Nossos resultados mostraram que a experiência dos desenvolvedores é uma das principais razões para a baixa produtividade. Além disso, os resultados indicaram formas para contornar este problema, como melhorar e incentivar o ensino de programação paralela em cursos de graduação.

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Applicability of the software cost model COCOMO II to HPC projects

Cited by 7 publications

References 0 publications

Valuation and pricing of software licenses to support supplier–buyer negotiations: A case study in the automotive industry

Valuation and pricing of software licenses to support supplier–buyer negotiations: A case study in the automotive industry

Self-paced Learning in HPC Lab Courses

Opinião de Brasileiros Sobre a Produtividade no Desenvolvimento de Aplicações Paralelas

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