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Semi-automated Algorithm for Complex Test Data Generation for Interface-based Regression Testing of Software Components

Potuzak Tomas, Lipka Richard

Annals of Computer Science and Information Systems

2021

Vol. 25

501--510

This paper describes in detail the Complex Object Generation (COG) algorithm, which is a semi-automated algorithm for the generation of instances of classes (i.e., objects) with a complex inner structure for Java and similar languages designed for black-box testing (i.e., without available source code). The algorithm was developed and tested as a stand-alone algorithm and can be used as such (e.g., during unit testing). However, we plan to use it to generate the parameter values of generated method invocations, which is a vital part of our interface-based regression testing of software components.

Tool-assisted surrogate selection for simulation models in energy systems

Balduin Stephan, Oest Frauke, Blank-Babazadeh Marita, Nieße Astrid, Lehnhoff Sebastian

Annals of Computer Science and Information Systems

2019

Vol. 18

185--192

Surrogate models have proved to be a suitable replacement for complex simulation models in various applications. Runtime considerations, complexity reduction and privacy concerns play a role in the decision to use a surrogate model. The choice of an appropriate surrogate model though is often tedious and largely dependent on the individual model properties. A tool can help to facilitate this process. To this end, we present a surrogate modeling process supporting tool that simplifies the process of generation and application of surrogate models in a co-simulation framework. We evaluate the tool in our application context, energy system co-simulation, and apply it to different simulation models from that domain with a focus on decentralized energy units.

Big data platform for smart grids power consumption anomaly detection

Lipcàk Peter, Macak Martin, Rossi Bruno

Annals of Computer Science and Information Systems

2019

Vol. 18

771--780

Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).