The Tools we built

The following list shows the tools that were build (and some of the datasets used) during SPP1593. The list will be updated continuously.



  • Tools and platforms for the CURES project. For usage knowledge, we provide a software development kit that enables developers to monitor usage data and collect feature crumbs. The CuuSE platform, which will be available soon, can receive and visualize this usage knowledge. For decision knowledge, we develop ConDec plug-ins for JIRA and Git that enable developers to manage decision knowledge. The developers can visualize the documented decision knowledge related to code, commit messages, and JIRA issues such as features, tasks to implement a feature, or bug reports. Both CuuSE and ConDec are integrated via a webhook system.



  • Kieker4DQL (Repo): Kieker4DQL is an adapter for the Descartes Query Language (DQL) that performs query processing for the Kieker application monitoring tool.
  • Open.xtrace (Repo): Open Execution Trace Exchange (OPEN.xtrace) is a format that enables data interoperability and exchange between application performance monitoring (APM) tools and software performance engineering (SPE) approaches.
  • CASPA (Repo): CASPA is a ready-to-use and extensible evaluation platform that already includes example applications and state-of-the-art SPE components, such as monitoring and model extraction.
  • fastpan: A framework for model-agnostic software performance analysis.
  • DQL (Repo): The Descartes Query Language (DQL) enables to query performance of a system using adapters to various solution approaches, decoupling the description of user concerns (performance questions and goals) from the task of selecting and applying a specific solution approach.
  • Kieker (Repo): Kieker provides complementary dynamic analysis capabilities, i.e., monitoring and analyzing a software system’s runtime behavior — enabling application performance monitoring and architecture discovery.
  • WESSBAS (): WESSBAS presents an approach that aims to automate the extraction and transformation of workload specifications for load testing and model-based performance prediction of session-based application systems.
  • <Benchflow (Benchflow): An open-source expert system for automated end-to-end decllarative performance testing and performance insights.
  • DML (Repo): The Descartes Modeling Language (DML) is an architecture-level modeling language for quality-of-service and resource management of modern dynamic IT systems and infrastructures.
  • PMX (Repo): The Performance Model eXtractor (PMX) tool automates the extraction of architectural performance models form measurement data.
  • QPME (Repo): QPME (Queueing Petri net Modeling Environment) is an open-source tool for stochastic modeling and analysis based on the Queueing Petri Net (QPN) modeling formalism.
  • SQuAT (Repo): SQuAT is an approach for concern-driven distributed multi-objective optimization of software architectures


Following tools were developed:
  • KAMP
  • KAMP4aPS
Tools can be found on KAMP-Website and on Github. Industry 4.0 interface implementation of the xPPU can be found on Github.


  • ConsistAnts - consistency checker for probabilistic software quality models
  • CoWolf - A Co-Evolution Tool
  • State Elimination as Model Transformation Problem - Tool and data set created for the TTC 2017
  • A Dataset of EMF Models from Eclipse Projects: Models are key artefacts in Model-driven software engineering. Data sets of models from practice are highly valuable as input for different modelling research areas, e.g., performance benchmarks for modelling tools and analysing model transformations, as well as in empirical research, e.g., understanding how models are designed and evolve over time. Unfortunately, there is a lack of data sets containing models, their meta models, and their evolution history. We present such a data set and describe our data collection method. The Eclipse modeling framework (EMF) is the major framework for developing and using EMF models providing a rich ecosystem developing many models and meta models. Thus, we mined meta models and their instances from git repositories associated with Eclipse projects (, accessed 2018-05-25), including their version history. Our data set was created on 2018-05-25 and contains 31799 models of which 4732 are meta models with a total of 101267 versions. These were mined from 247 repositories belonging to 130 projects hosted on Eclipse Projects.
  • Supporting Semi-Automatic Co-Evolution of Architecture and Fault Tree Models (Dataset): In the whole life-cycle of systems in safety-critical domains, system models must consistently co-evolve with quality evaluation models like fault trees. However, performing these co-evolution steps is a cumbersome and often manual task. To understand this problem in detail, we have analyzed the evolution and mined common changes of architecture and fault tree models for a set of evolution scenarios of a part of a factory automation system called pick&place unit. Based on the results, we could derive a set of co-evolution rules which fully cover the evolution scenarios of the case study and which offer the potential to semi-automate the co-evolution process. In particular, we evaluated these rules by a comparison to typical visual editor operations. Our results show a significant reduction of the amount of required user interactions in order to realize the co-evolution.
  • SRE-DTMC-Transformations - Tool for Incremental transformations between Stochastic Regular Expressions and Markov Chains






iObserve Analysis
Github Eclipse Repository
Generator Composition
Eclipse Repository
iObserve instrumented CoCoME
Eclipse Repository
Software Repository
Maven Repository
Github Dockerhub


  • SiLift: differencing, patching, slicing of models
  • ReVision: model repair
  • SiPL: delta-oriented model-based SPL Engineering
  • SERGe: generation of edit operations


Following tools were developed: