Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behaviour (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.

Software systems enabling large-scale data analysis workflows (DAWs) are a key technology for modern science as they allow extracting new insights from experimental results. DAWs are pipelines composed of interdependent tasks that are executed in a distributed fashion on large compute clusters. Typically, the individual task implementations are developed by research groups all over the world and usually not tested outside a narrow scope of possible inputs, parameters, and infrastructures. As a result, the operations' correctness depends on many implicit assumptions, such as the completeness and suitability of input data, infrastructure properties such as available cores, etc. This makes quality assurance of DAWs a critical issue. We propose to address this problem by introducing a contract-driven approach to DAW design and implementation. Following the well-known principle of Design by Contract, DAW developers specify contracts in the form of requirements and promises for each task of a DAW. These contracts serve as guards to ensure that tasks run in a proper environment and produce correct results. The detection of contract violations allows to halt the execution of a DAW and to identify the culprit that caused the violation. Thus, the integration of contracts into DAW design provides opportunities for efficiency improvements by reducing computation and debugging time in case of errors.

Software systems enabling large-scale data analysis workflows (DAWs) are a key technology for many scientific disciplines, as they allow extracting new insights from experimental results. DAWs are (non-)linear pipelines composed of multiple interdependent tasks that are executed in a distributed fashion on large compute clusters. In science, the individual task implementations are developed by research groups all over the world and usually not tested outside a narrow scope of possible inputs, parameters, and infrastructures. As a result, the operations' correctness depends on many implicit assumptions. Among others this includes the completeness and suitability of input data, infrastructure properties such as available cores or main memory, etc. This combination of complexity, distribution and untested components makes quality assurance of DAWs a critical issue. In this paper, we propose to address this problem by introducing a contract-driven approach to DAW design and implementation. Following this method, DAW developers specify contracts in the form of requirements and promises for each task of a DAW. These contracts serve as guards to ensure that tasks run in a proper environment and produce correct results. We provide the first formal definition of contracts for DAWs and show how they are connected to DAW scheduling and execution. As a proof of concept, we extended Nextflow, a popular scientific workflow system, with contracts and defined a light-weight DSL for their specification. We exemplify the power of a contract-driven approach to DAW development by enhancing several real-world DAWs from Bioinformatics to capture typical problems during their execution and show how the specific notifications issued by broken contracts help debugging the DAWs.

Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the components’ and tasks’ performance metrics, traces, and behavior are necessary to support the end user with a level of abstraction since the large amount of data cannot be analyzed manually. The execution and monitoring of scientific workflows involves many components, the cluster infrastructure, its resource manager, the workflow, and the workflow tasks. All components in such an execution environment access different monitoring metrics and provide metrics on different abstraction levels. The combination and analysis of observed metrics from different components and their interdependencies are still widely unregarded.We specify four different monitoring layers that can serve as an architectural blueprint for the monitoring responsibilities and the interactions of components in the scientific workflow execution context. We describe the different monitoring metrics subject to the four layers and how the layers interact. Finally, we examine five state-of-the-art scientific workflow management systems (SWMS) in order to assess which steps are needed to enable our four-layer-based approach.

Software testing is an important part of the software life-cycle. Unfortunately, some software systems have the inherent problem, that it is not clear a priori what the expected outcome is. This is known in the literature as the Oracle Problem. Scientific software suffers from this problem in particular. Metamorphic Testing is a testing approach that mitigates the Oracle Problem, as it is based on identified relations between a program's in- and output pairs. In this study, we investigate the feasibility of automatically finding such metamorphic relations on a software package known as the exciting-NOMAD parser which is widely used in computational material science. We are able to show that it is indeed possible to automatically find metamorphic relations within the NOMAD parser for the density functional theory code exciting. We analyse the metamorphic relations found through our tool in terms of both quantity and relation quality. Furthermore, we also publish our developed tool, as well as used data alongside this study through our replication package.

Analysis of data is the foundation of multiple scientific disciplines, manifesting in complex and diverse scientific data analysis workflows often involving exploratory analyses. Such analyses represent a particular case for traditional data engineering workflows, as results may be hard to interpret and judge whether they are correct or not, and where experimentation is a central theme. Oftentimes, there are certain aspects of a result which are suspicious and which should be further investigated to increase the trustworthiness of the workflow’s outcome. To this end, we advocate a semi-automated approach to reducing a workflow’s input data while preserving a specified outcome of interest, facilitating irregularity localization by narrowing down the search space for spotting corrupted input data or wrong assumptions made about it. We outline our vision on building engineering support for outcome-preserving input reduction within data analysis workflows, and report on preliminary results obtained from applying an early research prototype on a computational notebook taken from an online community of data scientists and machine learning practitioners.