Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process

Enterprise, project and workforce selection models for industry 4.0.

Abstract Enterprise, project, and workforce selection models for Industry 4.0. Rupinder Kaur The German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of advanced technologies such as cyber-physical system, internet of things, cloud computing, and cognitive computing with the aim to revolutionize the current manufacturing practices. Automation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous benefits, industries are readily investing in Industry 4.0, but this implementation is an uphill struggle. In this thesis, we address three key problems related to Industry 4.0 implementation namely Enterprise selection, Project selection and Workforce selection. The first problem involves identification of enterprises suitable for Industry 4.0 implementation. The second problem involves prioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and last problem involves workforce selection and assignment for execution of the identified Industry 4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are proposed to address these problems. Industry experts are involved to prioritize the criteria used for enterprise, project and workforce selection. Numerical applications are provided. The proposed work is innovative and can be useful to manufacturing and service organizations interested in implementing Industry 4.0 projects for performance improvement

A influência de fatores na produtividade do desenvolvimento de software de acordo com um modelo de estruturas teóricas

This work presents an evidence-based model describing the effects of a set of factors on software development productivity, obtained through an evidence synthesis method in Software Engineering. Thus, the relationships among this set and the software development productivity (observed phenomena) are described as results of combining theoretical structures capable of expressing and dealing with differences between different effects and uncertainties varying according to the types of studies found in the literature. Besides, to evaluate the model found, its findings are confronted with a survey capturing the practitioners’ perception (managers and leaders of software projects in Brazilian organizations). The degree of agreement between research (the model) and practice (the practitioners’ perception) shows that scientific knowledge does not differ considerably from the reality experienced by software projects when both of them refer to the influence of factors on software development productivity. The impression that research and practice on the theme go through different paths persists. According to this work, the reasons for this impression are more related to the use of non-standardized and, perhaps, inappropriate measures used to perceive and monitor the influence of factors as well as to measure the software development productivityEste trabalho apresenta um modelo baseado em evidências que descreve efeitos de alguns fatores na produtividade do desenvolvimento de software, obtidos através de um método de síntese de evidências em Engenharia de Software. Deste modo, as relações entre um conjunto de fatores e a produtividade do desenvolvimento de software (fenômenos observados) são descritas como resultados da combinação de estruturas teóricas capazes de expressar e tratar diferenças entre efeitos e incertezas variadas de acordo com os tipos de estudos primários encontrados na literatura. Além disso, para avaliar o modelo encontrado, seus achados são confrontados com uma pesquisa de opinião realizada para capturar a percepção de profissionais da prática (gestores e líderes de projetos de software em organizações brasileiras). O grau de concordância entre a pesquisa (o modelo) e a prática (a percepção dos profissionais) demonstra que, aparentemente, o conhecimento científico não diverge consideravelmente da realidade vivenciada pelos projetos de software no Brasil, quando ambos se referem à influência de fatores na produtividade do desenvolvimento de software. Persiste a impressão, entretanto, de que a pesquisa e a prática no tema percorrem caminhos distintos. De acordo com este trabalho, a impressão do distanciamento parece estar relacionadas à questão do uso de medidas não-padronizadas e, talvez, inapropriadas para mensurar os fatores e a produtividade do desenvolvimento de softwar