Employing machine learning techniques to assess requirement change volatility

Abstract

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.

Identification of Influential Modules Considering Design Change Impacts Based on Parallel Breadth-First Search and Bat Algorithm

“…It enables the product to fulfill affluent market segments, but at the same time, increase product design complexity. If properly managed, changes can provide opportunities for improvement to the product and increase its consumer value ( Hein et al, 2021 ). A change may encompass any modification of the product as a whole or in part, and may alter the interactions and dependencies of the constituent elements of the product ( Jarratt et al, 2011 ).…”

“… Lee et al (2010) employed the analytic network process to measure design change impacts in modular products, and identified the final priorities of parts with their relative change impacts on the whole product. Ullah et al ( Hein et al, 2021 ) analyzed effective change propagation quantitative risk-based in a product family design, and adopted a seven-step mechanism comprising of a mathematical model and an algorithm. This approach takes into account direct and indirect change propagation, but does not analyze change impact degree between modules.…”

“…Some tools and methods for assessing or predicting the influence of engineering changes on products have been proposed in extant literature ( Ollinger and Stahovich, 2004 ; Yang and Duan, 2012 ; Ahmad et al, 2013 ; Hamraz and Clarkson, 2015 ). However, they tend to be confined to an integrated product architecture or a single product instance ( Ollinger and Stahovich, 2004 ; Lee et al, 2010 ; Yang and Duan, 2012 ; Ahmad et al, 2013 ; Hamraz and Clarkson, 2015 ; Hein et al, 2021 ). This paper aims at presenting an approach to analyzing the associations and change impacts between modules in modular products based on design structure matrix and change propagation network.…”

Automated Requirement Dependency Analysis for Complex Technical Systems

“…Existing research findings indicate that the dependency types requires, is required by and is refined by propagate inevitably, whereas other dependency type have no or instable correlation to change propagation (Goknil et al, 2014;Zhang et al, 2014;. Today, existing approaches mostly exist in Engineering Change Management (ECM) (Hamraz et al, 2013;Mehr et al, 2021) and Requirement Change Management (RCM) (Jayatilleke and Lai, 2018;Hein, P. H. et al, 2021) and have shortcomings regarding availability of required data in early development stages, differentiation of dependency types, higher order change propagation and ability to process large requirement sets . The contribution at hand aims to address this research gap.…”

A systematic mapping study on machine learning methodologies for requirements management

“…[12] used the logistic regression approach to predict possible requirements changes in embedded systems; Phyo et al. [13] used the multi‐label learning approach to predict the possible spread of requirements changes; Kelly et al. [14] used a random forest model to predict possible changes of requirements during an iterative process.…”