Logo des Repositoriums
 

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

Kurzanzeige
dc.contributor.authorWiesweg, Florian
dc.contributor.authorVogelsang, Andreas
dc.contributor.authorMendez, Daniel
dc.contributor.editorKoziolek, Anne
dc.contributor.editorSchaefer, Ina
dc.contributor.editorSeidl, Christoph
dc.date.accessioned2020-12-17T11:58:05Z
dc.date.available2020-12-17T11:58:05Z
dc.date.issued2021
dc.description.abstractThis paper has been accepted at the 2020 IEEE Requirements Engineering Conference (RE). 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 post-mortem (diagnostic) analysis, deriving probable causes of sub-optimal 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 its applicability to real-world scenarios with a case study.en
dc.identifier.doi10.18420/SE2021_47
dc.identifier.isbn978-3-88579-704-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34544
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2021
dc.relation.ispartofseriesecture Notes in Informatics (LNI) - Proceedings, Volume P-310
dc.subjectRequirements Engineering
dc.subjectData-Driven RE
dc.subjectRisk Management
dc.titleData-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networksen
dc.typeText/ConferencePaper
gi.citation.endPage120
gi.citation.publisherPlaceBonn
gi.citation.startPage119
gi.conference.date22.-26. Februar 2021
gi.conference.locationBraunschweig/Virtuell

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
B1-46.pdf
Größe:
55.42 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P310 - Software Engineering 2021