Constructing the graphical structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study

Abstract

Context:

In scenarios where data availability issues hinder the applications of statistical causal modeling in software engineering (SE), Bayesian networks (BNs) have been widely used due to their flexibility in incorporating expert knowledge. However, the general understanding of how the graphical structure, i.e., the directed acyclic graph (DAG), of these models is built from domain experts is still insufficient.

Objective:

This study aims to characterize the SE landscape of constructing the graphical structure of BNs, including their potential for causal modeling.

Method:

We conducted a systematic mapping study employing a hybrid search strategy that combines a database search with parallel backward and forward snowballing.

Results:

Our mapping included a total of 106 studies. Different methods are commonly combined to construct expert-based BN structures. These methods span across data gathering & analysis (e.g., interviews, focus groups, literature research, grounded theory, and statistical analysis) and reasoning mechanisms (e.g., using idioms combined with the adoption of lifecycle models, risk-centric modeling, and other frameworks to guide BN construction). We found a lack of consensus regarding validation procedures, particularly critical when modeling cause–effect relationships from knowledge. Additionally, expert-based BNs are mainly applied at the tactical level to address problems related to software engineering management and software quality. Challenges in creating expert-based structures include validation procedures, experts’ availability, expertise level, and structure complexity handling. Key recommendations involve empirical validation, participatory involvement, and balance between adaptation to organizational constraints and model construction requirements.

Conclusion:

The construction of expert-based BN structures in SE varies in rigor, with some methods being systematic while others appear ad hoc. To enhance BN application, reducing expert knowledge subjectivity, enhancing methodological rigor, and clearly articulating the construction rationale is essential. Addressing these challenges is crucial for improving the reliability of causal inferences drawn from these models, ultimately leading to better-informed decisions in SE practices.