Secondary studies on human aspects in software engineering: A tertiary study

Zolduoarrati, Elijah; Licorish, Sherlock A.; Stanger, Nigel

doi:10.1016/j.jss.2023.111654

Cited by 4 publications

(1 citation statement)

References 70 publications

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“…In our search, we initially considered using the terms "software engineering" and "software development" as the SE-related subset of our search string, following the practices of other SE tertiary studies (e.g., [5,6,24]). However, recognizing that these specific terms might not always be present in the papers, we used ChatGPT to identify the most common SE problems addressed using BNs and integrated its insights to broaden our search scope.…”

Section: Limitationsmentioning

confidence: 99%

Shedding Light on the Techniques for Building Bayesian Networks in Software Engineering

Rique,

Dantas,

Albuquerque

et al. 2023

Anais Do III Workshop Brasileiro De Engenharia De Software Inteligente (ISE 2023)

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Context Bayesian networks (BNs) have been used to tackle several software engineering (SE) problems, such as risk management and effort estimation. They enable reasoning under uncertainty and have the advantage of incorporating expert knowledge to build more accurate models when sufficient historical data are not available. Software practitioners often encounter a lack of substantial evidence concerning the usability, limitations, risks, and benefits of BNs, as is the case with many other topics in the SE literature. Therefore, there is a need to organize and systematize the existing knowledge in this area. Objective This paper aims to provide researchers and practitioners with an overview of the techniques for building BNs in SE. Method We conducted a tertiary study following the guidelines available in the SE literature. Results We examined six secondary studies. Our findings revealed that expert knowledge emerges as the predominant technique for structure learning and, in conjunction with learning from data using automated tools, is widely employed for parameter learning in BNs. Conclusion Despite the attention given to data-driven approaches in SE, it is worth acknowledging the significant value that expert knowledge continues to hold in constructing more accurate and robust models. This observation underscores potential opportunities for developing expert-driven solutions to enhance model building and foster the adoption of BNs in the software industry.

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Section: Limitationsmentioning

confidence: 99%