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Systematic exploration of hypotheses is a major part of any empirical research. In software engineering, we often produce unique tools for experiments and evaluate them independently on different data sets. In this paper, we present…
Variability-aware metrics are designed to measure qualitative aspects of software product lines. As we identified in a prior SLR \cite{El-SharkawyYamagishi-EichlerSchmid19}, there exist already many metrics that address code or variability…
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various…
Kernel methods have proven to be powerful techniques for pattern analysis and machine learning (ML) in a variety of domains. However, many of their original or advanced implementations remain in Matlab. With the incredible rise and adoption…
Software product lines (SPL) are a method for the development of variant-rich software systems. Compared to non-variable systems, testing SPLs is extensive due to an increasingly amount of possible products. Different approaches exist for…
A Software Product Line (SPL) aims at applying a pre-planned systematic reuse of large-grained software artifacts to increase the software productivity and reduce the development cost. The idea of SPL is to analyze the business domain of a…
Processing high-throughput DNA sequencing data of individuals or populations requires stringing together independent software tools with many parameters, often leading to non-reproducible pipelines and datasets. We developed grenepipe to…
Analyses of a software product line (SPL) typically report variable results that are annotated with logical expressions indicating the set of product variants for which the results hold. These expressions can get complicated and difficult…
We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of…
Building high-quality knowledge graphs (KGs) from diverse sources requires combining methods for information extraction, data transformation, ontology mapping, entity matching, and data fusion. Numerous methods and tools exist for each of…
HAL is an open-source framework for gate-level netlist analysis, an integral step in hardware reverse engineering. It provides analysts with an interactive GUI, an extensible plugin system, and APIs in both C++ and Python for rapid…
Software product line (SPL) engineering has been successfully applied to software development by obtaining software systems as compositions of modular features. Existing approaches to SPL engineering, however, are typically bound to a…
We propose a framework that provides a programming interface to perform complex dynamic system-level analyses of deployed production systems. By leveraging hardware support for virtualization available nowadays on all commodity machines,…
Safety-critical software systems are in many cases designed and implemented as families of products, usually referred to as Software Product Lines (SPLs). Products within an SPL vary from each other in terms of which features they include.…
CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that…
The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand…
In the literature, the definition of product in a Software Product Line (SPL) is based upon the notion of consistency of the constraints, imposed by variability and traceability relations on the elements of the SPL. In this paper, we…
ConsumerCheck is an open source data analysis software tailored for analysis of sensory and consumer data. Since some of the implemented methods are generic, such as PCA, PLSR and PCR, other data from other domains may also be analysed with…
Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering,…
This paper presents a novel approach to the design verification of Software Product Lines(SPL). The proposed approach assumes that the requirements and designs are modeled as finite state machines with variability information. The…