## 3 Method for Proactive Management of Requirement Changes
### Change Risk Assessment
#### 3.2.3 Requirement Change Likelihood
To reduce the application effort, this can be carried out for sections of related requirements (e.g., requirements on a certain system component) and automatically transferred to requirements of this section. 2. Selecting reference projects  
After relevant change initiators are assigned to the requirements, historical change data--number of changes differentiated by initiator--from development projects with equivalent circumstances (reference projects) are analyzed. The goal is to increase the significance of considered data for the requirements investigated. To identify reference projects, twelve project characteristics as well as a short project descriptions are captured and compared. Exemplary characteristics are degree of novelty, type of customer (new or stock customer) and complexity of the system (cf. Appendix E). Selection of project characteristics considered availability of required information at the project start and influence on change initiators. Capturing of characteristics and selection of reference projects are carried out manually by experts. 3. Calculating exogenous change likelihood  
To calculate the exogenous change likelihood, historical change data from all reference projects are merged. Then, the number or changes from relevant change initiators are added up for each requirement. Divided by the overall number of changes, the exogenous change likelihood is derived (c.f. Equations (3) and (4)). \[P^{*}_{exogenous}(r_{i})\ =\ \frac{\sum\textit{number of changes from relevant change initiators of a requirement}}{\textit{number of changes}} \tag{3}\]  
\[0\ \leq\ P^{*}_{exogenous}(r_{i})\ \leq\ \frac{\textit{number of exogenous changes}}{\textit{number of changes}}\ \leq\ 1 \tag{4}\]  
\(P^{*}_{exogenous}(r_{i})\) is a characteristic value for the exogenous change likelihood and does not equate an absolute likelihood. The higher the value, the higher the likelihood of a requirement change triggered by an exogenous change initiator. Endogenous changes are triggered by change propagation within the requirement network. Therefore, a measure based on a requirement's connectivity in terms of change propagation is needed. Connectivity can be assessed in two alternative ways: step by step analysis of single propagation paths or holistic analysis of the requirement set. Path specific analysis can be carried out based on [PERSON]'s laws [68]. It requires to determine the likelihood of a requirement change caused by propagation for each path and each requirement one by one. Although this method is accurate, it requires too much processing power for extensive requirement sets. Therefore, a holistic analysis is preferred. This can be carried out based on a the previously introduced modified PageRank algorithm in a very efficient manner, even for extensive requirement sets. In difference to the Active Rank for impact analysis (c.f. Equation (5)), which is calculated based on outgoing dependencies, a Passive Rank is needed to indicate the endogenous change likelihood. This is achieved by considering the incoming dependencies--mathematically by keeping the signs equally to the original PageRank. \[PR(r_{i})\ =\ \frac{1\ -\ d}{n}\ +\ d\sum\
olimits_{\tau_{j}\in E_{i}^{in}}( \sum\
olimits_{\tau_{k}\in E_{j}^{out}}\frac{PR(r_{j})}{v_{kj}}) \tag{5}\]  
To derive a likelihood distribution, the Passive Rank is normed following the random surfer principle [63; 64]. Each requirement's Passive Rank is divided by the maximum Passive Rank (cf. Equation (6)). \[PR^{\prime}(r_{i})\ =\ \frac{PR(r_{i})}{\max(PR)} \tag{6}\]  
To relate the endogenous change likelihood to the exogenous change likelihood \(P^{*}_{exogen,\ i^{\prime}}\) the normed Passive Rank is multiplied with the quotient of endogenous changes from all changes (cf. Equations (7) and (8).