期刊
2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE)
卷 -, 期 -, 页码 173-182出版社
IEEE
DOI: 10.1109/ISSRE.2019.00026
关键词
Reliability; Security; Machine Learning; Failure Prediction; Vulnerability Prediction
资金
- H2020 project ATMOSPHERE [777154]
- FCT [SFRH/BD/140221/2018]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/140221/2018] Funding Source: FCT
The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently used in the dependability domain, both for supporting systems design and verification activities. However, using ML is complex and highly dependent on the problem in hand, increasing the probability of mistakes that compromise the results. In this paper, we introduce Propheticus, a ML framework that can be used to create predictive models for reliable and secure software systems. Propheticus attempts to abstract the complexity of ML whilst being easy to use and accommodating the needs of the users. To demonstrate its use, we present two case studies (vulnerability prediction and online failure prediction) that show how it can considerably ease and expedite a thorough ML workflow.
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