Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.

中文翻译：

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采用机器学习技术来评估需求变化的波动性

在更改需求以反映利益相关者的期望时缺乏计划会导致传播的更改，从而导致项目失败。现有工具无法提供管理需求变更和最小化意外变更传播所需的正式推理。本研究探索了机器学习技术，以基于需求网络可用于研究变更传播的前提，使用复杂的网络指标来预测需求变更波动性 (RCV)。解决了三个研究问题 (RQ)：(1) 在每个变化实例中，能否通过四类即乘法器、吸收器、发射器和鲁棒性来测量 RCV？(2) 能否在每次变更实例中为每个需求探索和计算复杂的网络指标？(3) 机器学习技术能否，具体来说，多标签学习 (MLL) 方法可用于使用复杂网络指标预测 RCV？本文中的 RCV 量化了变更传播的波动性，即需求如何响应初始变更。乘数是一种需求，它被初始更改更改并将更改传播到其他需求。吸收器是由初始更改更改的需求，但不会将更改传播到其他需求。发送器是一种不会因初始更改而更改的需求，但会将更改传播到其他需求。健壮的需求是不因初始更改而更改并且不会将更改传播到其他需求的需求。RCV 是使用从先前开发的精炼自动化需求变更传播预测 (R-ARCPP) 工具中获得的工业数据和需求网络关系确定的。讨论了最高性能机器学习模型中有用的复杂网络指标以及本研究的局限性和未来方向。