Machine Learning based Static Code Analysis for Software Quality Assurance | LSWI
09/2018 Proceedings

Sultanow, Eldar | Ullrich, André | Konopik, Stefan | Vladova, Gergana

Machine Learning based Static Code Analysis for Software Quality Assurance

Abstract

Machine Learning is often associated with predictive analytics, for example with the prediction of buying and termination behavior, with maintenance times or the lifespan of parts, tools or products. However, Machine Learning can also serve other purposes such as identifying potential errors in a mission-critical large-scale IT process of the public sector. A delay of troubleshooting can be expensive depending on the error’s severity – a hotfix may become essential. This paper examines an approach, which is particularly suitable for Static Code Analysis in such a critical environment. For this, we utilize a specially developed Machine Learning based approach including a prototype that finds hidden potential for failure that classical Static Code Analysis does not detect.

Kategorie Proceedings
Autoren Sultanow, Eldar; Ullrich, André; Konopik, Stefan; Vladova, Gergana
Bandtitel Proceedings of the Thirteenth International Conference on Digital Information Management (ICDIM 2018) IEEE
Datum 09/2018
pp. 156 - 161
Verlag IEEE
DOI 10.1109/ICDIM.2018.8847079
Keywords association rule mining, Machine Learning, Static Code Analysis, German Federal Employment Agency