## 3 Method for Proactive Management of Requirement Changes
### Change Risk Assessment
#### 3.2.2 Requirement Change Impact Assessment
The categorization of the dependencies according to propagation behavior is used to weight the edges in the requirement network and thus to enable a statement about the consecutive effects (cf. Figure 6). Compared to existing approaches with binary differentiation regarding the existence of a dependency, the differentiation of the dependency types allows a more precise evaluation. The result of this step is weighted edges in the requirement network as an indicator for consecutive effects of a requirement change. Using the weighted edges (dependencies) in the requirement network, propagation paths can be identified and the requirements lying on them can be examined with regard to local change effects. Predictions about the local effects are used to weight the nodes in the requirement network (cf. Figure 6). The requirement priority was selected as an indicator for the local effects of a requirement change. The priority allows one to conclude the significance for project success and has two advantages: high availability in the requirements data and ability to be evaluated automatically. The alternative is a manual rating by experts. Presumably this leads to higher accuracy but causes high application effort, since every requirement need to be rated individually. Within the preliminary study, manual rating was judged by users to be too time-consuming for industrial practice. Therefore, the requirement priority is used. Depending on the company-specific prioritization scheme, weighting factors can be defined. Since many prioritization schemes follow a threefold structure (for instance, enthusiasm characteristic, performance characteristic and basic characteristic) [61], a differentiation into high (weighting = 3), medium (weighting = 2) and low priority (weighting = 1) is used as an example. The result of this step is weighted nodes in the requirement network as an indicator for local effects of a change. Analysis of Requirement Network  
The weighted requirement network is evaluated automatically regarding expected change impact. Critical requirements are selected. Evaluation of the requirement network is based on the PageRank algorithm [55] (based on [62; 63]). Context-specific adaptation of the original PageRank algorithm is required to account for the weighting of dependencies and requirement as well as the direction of dependencies. For impact analysis, only the outgoing dependencies of a requirement are relevant, since only such dependencies can be used for change propagation. The basic PageRank function is the efficient analysis of dependencies in big networks. The aim is to determine a ranking as a statement about the connectivity of an element within the network. The original functionality of the PageRank algorithm according to  
Figure 6: Weighting of dependencies (edges) and requirements (nodes) within the requirement network. [PERSON] and Page [64] is an iterative adjustment of the connectivity value of an element \(PR(x)\) based on outgoing and incoming dependencies. The iterative calculation is terminated as soon as the connectivity values are stable. The computational logic of the PageRank algorithm was developed to determine a ranking of results for web search engines [64] and is highly efficient in analyzing a large number of network elements while considering higher order dependencies. Thus, the algorithm solves the problem of existing solution approaches, which have an exponential increase in required processing effort per network element added [63]. For the modified PageRank algorithm (cf. Equation (1)), the weighting of the dependency \(v_{ij}\) between two requirements \(r_{i}\) and \(r_{j}\) is considered in a first step. The number of incoming dependencies \(E_{i}^{in}\) and outgoing dependencies \(E_{i}^{out}\) of a requirement \(r_{i}\) is also considered unchanged. As an initial value for the iterative adaptation, an equal weighting of the requirements is assumed \(\frac{1}{n}\), where n is the total number of requirements in the network.