Comparing Change Management Processes for Requirements and Manufacturing: An Interview Based Study

The study presented in this paper compares requirement and manufacturing change management processes to determine if similar processes can be used for both types of changes. A literature review is used to identify prescribed process stages. Ten stages are identified for both requirements and manufacturing change management. A series of interviews are then conducted with three different population groups to determine the process stages actually used in the field. The resulting process models are compared with the process models from the literature. Further, a thematic analysis is performed on the interview findings. Ultimately, differences are found between the prescribed and practiced change management models for both types of changes. Formal documentation stages are more prevalent for the manufacturing domain, though documentation in practice is less than what is prescribed. This includes the issuance of change requests and change orders in manufacturing change management that are not present in requirement change management processes. Significant differences were also found between the two change types; namely, requirement changes deal with more abstract concepts and as such can afford more informal documentation, whereas manufacturing changes deal with existing artifacts and require solid documentation. Additional research thrusts are identified to help reconcile change management processes across the life cycle.

Employing machine learning techniques to assess requirement change volatility

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.

Method for Systematic Assessment of Requirement Change Risk in Industrial Practice

Requirement changes and cascading effects of change propagation are major sources of inefficiencies in product development and increase the risk of project failure. Risk management regarding these requirement changes yields the potential to handle such changes efficiently. Currently unlocked, a systematic approach is required for risk management to assess the risk of a requirement change with appropriate effort in industrial application. Within the paper at hand, a novel method for systematic assessment of requirement change risk is presented. It is developed in a multiple case study approach with three product development projects from different industrial branches. The change risk is assessed by combining change likelihood and change impact. Propagation effects are considered by analyzing requirement interrelations. To limit application effort, a tailorable approach towards assessment of change causes based on generalized influence factors and a pre-defined rule set for semi-automatized assessment of requirements interrelations is used. A software prototype is developed and implemented to enable evaluation and transfer to industrial application. The approach is evaluated using a combination of case study projects, stakeholder workshops, questionnaires and semi-structured interviews. Applying the method, the risks of requirement changes are assessed systematically, and subsequent risk management is enabled. The contribution at hand opens up the research space of risk management in handling requirement changes which is currently almost unexploited. At the same time, it enables more efficient product development.