Sultanow, Eldar | Ullrich, André | Konopik, Stefan | Vladova, Gergana
Machine Learning based Static Code Analysis for Software Quality Assurance
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.
|Sultanow, Eldar; Ullrich, André; Konopik, Stefan; Vladova, Gergana
|Proceedings of the Thirteenth International Conference on Digital Information Management (ICDIM 2018) IEEE
|156 - 161
|association rule mining, Machine Learning, Static Code Analysis, German Federal Employment Agency