Employing machine learning techniques to assess requirement change volatility
| Autor: | Beshoy Morkos, Cheng Chen, Elisabeth Kames, Phyo Htet Hein |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Mechanical Engineering 05 social sciences Transmitter 050301 education 02 engineering and technology Complex network Machine learning computer.software_genre Industrial and Manufacturing Engineering 020901 industrial engineering & automation Architecture Premise Change propagation Multiplier (economics) Research questions Artificial intelligence Volatility (finance) business Engineering design process 0503 education computer Civil and Structural Engineering |
| Zdroj: | Research in Engineering Design. 32:245-269 |
| ISSN: | 1435-6066 0934-9839 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |