Automated Software Quality Monitoring in Research Collaboration Projects

  • In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.

Export metadata

Additional Services

Share in X Search Google Scholar
Metadaten
Author:Michael SildatkeORCiD, Hendrik Karwanni, Bodo Kraft, Oliver Schmidts, Albert Zündorf
DOI:https://doi.org/10.1145/3387940.3391478
Parent Title (English):ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops
Publisher:IEEE
Place of publication:New York, NY
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
First Page:603
Last Page:610
Note:
ICSE '20: 42nd International Conference on Software Engineering, Seoul, Republic of Korea, 27 June 2020 - 19 July 2020
Link:https://doi.org/10.1145/3387940.3391478
Zugriffsart:campus
Institutes:FH Aachen / Fachbereich Energietechnik
FH Aachen / Fachbereich Medizintechnik und Technomathematik
collections:Verlag / IEEE