Like other software, DAWs may show unexpected behavior or even crash due to various reasons. Debugging aims at establishing a cause effect relationship between the observable problem and the actual error. Such error identification serves as an initial step of a reliable problem resolution, and thus debugging of DAWs is an indispensable task to increase the dependability of DAWs. However, debugging DAWs is particularly challenging due to the heterogeneous nature of the involved tasks and the distributed nature of the execution engine. The central research question addressed in this subproject is how to enable domain scientists to efficiently formulate, test, and refine a debugging hypothesis in the context of scientific software engineering. It will primarily work together with A3 on the adaptation of software test technologies to distributed DAWs and with B6 on the distributed monitoring of DAW executions. The subproject will be coordinated by Prof. Kehrer, an expert in model-based software development, and Prof. Markl, an expert in large-scale distributed data analytics.
Towards Advanced Monitoring for Scientific Workflows Inproceedings
In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), IEEE, 2022.
Outcome-Preserving Input Reduction for Scientific Data Analysis Workflows Inproceedings Forthcoming
Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds? Journal Article
In: ACM SIGMOD Record, vol. 51, no. 1, pp. 1-8, 2022.
Fast datalog evaluation for batch and stream graph processing Journal Article
In: World Wide Web, vol. 25, pp. 971-1003, 2022.
In: 2022 IEEE 18th International Conference on e-Science (e-Science), pp. 521-528, 2022.