论文信息 - Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System

Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System

The paper presents an industrial application of a DevOps process for a Tax fraud detection system. In particular, we report the influence of the quality assessment during development iterations, with special focus on the fulfillment of performance requirements. We investigated how to guarantee quality requirements in a process iteration while new functionalities are added. The experience has been carried out by practitioners and academics in the context of a project for improving quality of data intensive applications.

[1] Helmut Krcmar,et al. Model-based performance evaluations in continuous delivery pipelines , 2015, QUDOS@SIGSOFT FSE.

[2] Wilhelm Hasselbring,et al. Performance-oriented DevOps: A Research Agenda , 2015, ArXiv.

[3] Dana Petcu,et al. DICE: Quality-Driven Development of Data-Intensive Cloud Applications , 2015, 2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering.

[4] Jeff Carpenter,et al. Cassandra: The Definitive Guide , 2010 .

[5] Nathan Marz,et al. Big Data: Principles and best practices of scalable realtime data systems , 2015 .

[6] Simona Bernardi,et al. Model-Driven Dependability Assessment of Software Systems , 2013, Springer Berlin Heidelberg.

[7] Helmut Krcmar,et al. PET: continuous performance evaluation tool , 2016, QUDOS@ISSTA.

[8] Ralph Johnson,et al. design patterns elements of reusable object oriented software , 2019 .

[9] Dean Leffingwell,et al. Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise , 2011 .

[10] José Merseguer,et al. Towards a UML profile for data intensive applications , 2016, QUDOS@ISSTA.

[11] James G. Shanahan,et al. Large Scale Distributed Data Science using Apache Spark , 2015, KDD.

[12] References , 1971 .