Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks

TL;DR

This paper presents a data-driven approach using Bayesian Networks trained on survey data to improve risk management in Requirements Engineering by diagnosing issues and predicting potential problems in projects.

Contribution

It introduces a novel application of Bayesian Networks trained on empirical survey data to support both diagnostic and predictive risk management in Requirements Engineering.

Findings

Bayesian Networks effectively identify causes of RE problems.

The approach predicts potential RE issues in early project stages.

Models show high accuracy in cross-validation.

Abstract

Requirements Engineering (RE) is a means to reduce the risk of delivering a product that does not fulfill the stakeholders' needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert's experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a postmortem (diagnostic) analysis, deriving probable causes of suboptimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing