Proactive Management of Requirement Changes in the Development of Complex Technical Systems

Abstract

Requirement changes and cascading effects of change propagation are major sources of inefficiencies in product development and increase the risk of project failure. Proactive change management of requirement changes yields the potential to handle such changes efficiently. A systematic approach is required for proactive change management to assess and reduce the risk of a requirement change with appropriate effort in industrial application. Within the paper at hand, a novel method for Proactive Management of Requirement Changes (ProMaRC) is presented. It is developed in close collaboration with industry experts and evaluated based on workshops, pilot users’ feedback, three industrial case studies from the automotive industry and five development projects from research. To limit the application effort, an automated approach for dependency analysis based on the machine learning technique BERT and semi-automated assessment of change likelihood and impact using a modified PageRank algorithm is developed. Applying the method, the risks of requirement changes are assessed systematically and reduced by means of proactive change measures. Evaluation shows high performance of dependency analysis and confirms the applicability and usefulness of the method. This contribution opens up the research space of proactive risk management for requirement changes which is currently almost unexploited. It enables more efficient product development.

Proactive Project Management of Logistics Enterprises

“…Концепт проактивного управління організацією в умовах реального часу забезпечує ранню діагностику проблемних ситуацій та впровадження заходів їх запобіганню [13].…”

Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review

“…Feature model [18], [19]; Pattern-based model [20], [21]; MAPE-K model [22], [23]; Agent / multiagent-based model [24] - [28]; Viewpoint model [29] , [30]; Problem-Based model [31], [32]; Continues Collaborative Model (CCM) [33]; Integrated-Secure SDLC model (IS-SDLC) [34]; Requirements frame model [35]; Grid Model [36]; Probabilistic model checking [37]; BIP (Behavior -Interaction -Priorities) Model [38]; [29], [39] model based testing; [40] model driven approach (MDA); [20] Model Driven Development (MDD); BERT Model [41] - [43]; RE-BERT Model [44]; Object-Role Modelling (ORM) notation [45]; i* model [46], [47]; scenario based modeling [48]; QR (Quality Requirements) Pattern [49] Annotated BPMN Model [50] Specific Method…”

“…Matching algorithm [62]; Quantum Algorithm [63]; Fuzzy Logic [64] [65]; PageRank Algorithm [41]; Decision Support [66], [67], [28]; Markov decision [68]; Genetic Algorithm [69], [70]; Horspool's Algorithm [71]; Keyphrase algorithm [72]; Naïve Bayes's Algorithm [73] Specific technique/Tools using tool Nomos and NomosT [74]; Automated Planning Technique [75]; Ethnographical [76]; DT-Golog [68]; 4.…”

Model-Based Effect-Chain Analysis for Complex Systems

“…tasks and persons) (Danilovic and Browning 2007). Depending on the complexity of the system, the application effort to manually fill these matrices exceeds a reasonable level (Gräßler et al 2022). An alternative approach to map artifacts and capture dependencies is a system model.…”