Modern software development relies on a combination of development and re-use of technical asset, e.g., software components, libraries, and APIs. In the past, re-use was mostly conducted with internal assets but today external; open source, customer off-the-shelf (COTS), and assets developed through outsourcing are also common. This access to more asset alternatives presents new challenges regarding what assets to optimally chose and how to make this decision. To support decision-makers, decision theory has been used to develop decision models for asset selection. However, very little industrial data has been presented in literature about the usefulness, or even perceived usefulness, of these models. Additionally, only limited information has been presented about what model characteristics determine practitioner preference toward one model over another. The objective of this work is to evaluate what characteristics of decision models for asset selection determine industrial practitioner preference of a model when given the choice of a decision model of high precision or a model with high speed. An industrial questionnaire survey is performed where a total of 33 practitioners, of varying roles, from 18 companies are tasked to compare two decision models for asset selection. Textual analysis and formal and descriptive statistics are then applied on the survey responses to answer the study’s research questions. The study shows that the practitioners had clear preference toward the decision model that emphasized speed over the one that emphasized decision precision. This conclusion was determined to be because one of the models was perceived faster, had lower complexity, was more flexible in use for different decisions, and was more agile on how it could be used in operation, its emphasis on people, its emphasis on “good enough” precision and ability to fail fast if a decision was a failure. Hence, we found seven characteristics that the practitioners considered important for their acceptance of the model. Industrial practitioner preference, which relates to acceptance, of decision models for asset selection is dependent on multiple characteristics that must be considered when developing a model for different types of decisions such as operational day-to-day decisions as well as more critical tactical or strategic decisions. The main contribution of this work are the seven identified characteristics that can serve as industrial requirements for future research on decision models for asset selection.

中文翻译：

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影响资产选择决策模型偏好的特征：一项行业问卷调查

现代软件开发依赖于技术资产（例如软件组件、库和 API）的开发和重用的组合。过去，重用主要是通过内部资产进行的，但今天是外部的；开源、客户现成 (COTS) 和通过外包开发的资产也很常见。这种对更多资产替代方案的访问带来了关于最佳选择哪些资产以及如何做出此决定的新挑战。为了支持决策者，决策理论已被用于开发资产选择的决策模型。然而，关于这些模型的有用性甚至感知有用性的文献中提供的工业数据非常少。此外，关于哪些模型特征决定了从业者对一种模型的偏好而不是另一种模型，仅提供了有限的信息。这项工作的目的是评估在给定高精度或高速决策模型时，资产选择决策模型的哪些特征决定了行业从业者对模型的偏好。进行了一项行业问卷调查，共有来自 18 家公司的 33 名不同角色的从业者被要求比较两种资产选择的决策模型。然后将文本分析以及正式和描述性统计应用于调查回复，以回答研究的研究问题。研究表明，从业者明显偏爱强调速度的决策模型，而不是强调决策精度的决策模型。这个结论被确定是因为其中一个模型被感知得更快，复杂度更低，在不同决策的使用上更灵活，在如何在操作中使用更灵活，它强调人，它强调“足够好”的精度和快速失败的能力，如果决策失败。因此，我们发现了从业者认为对他们接受该模型很重要的七个特征。工业从业者偏好，这与接受有关，资产选择的决策模型取决于多种特征，在为不同类型的决策（例如运营日常决策以及更关键的战术或战略决策）开发模型时必须考虑这些特征。这项工作的主要贡献是确定的七个特征，这些特征可以作为未来资产选择决策模型研究的行业要求。