News

Student Assistant Position for our Open-Assess Interaction Datasets Project

21.07.2026
We are currently looking for a student assistant to support our research project "Towards Realistic Keyboard, Mouse, and Touch User Studies: Creating Open-Access Datasets for Interaction Research". The project focuses on increasing the availability of open-access datasets to support scalable user interaction research and foster greater transparency and reproducibility. Applicants must be enrolled at the University of Göttingen. The student assistant will support:
  • Dataset preparation and validation
  • Experiments with foreign datasets
  • Preparation of user studies
  • Acquiring participants for the study
  • Proofreading text and verifying checklists for data sharing
Applicants should have:
  • Good communication skills (in English or German)
  • Programming skills
  • Experience with either data work or user studies will be beneficial but is not required
The position is available starting in August 2026 and runs till the end of 2026. Working hours are flexible and everything between 5 and 20 hours per week during the semester or up to 40 hours during semester breaks is possible. The start date will be agreed upon individually. Compensation is at the standard HiWi rate. Applications are reviewed on a rolling basis, so early applications are encouraged. If you're interested in joining our team, please send your application (a brief introduction about yourself, including your transcript of records) to: carina.bieberatcs.uni-goettingen.de.

Project Funding for Open-Access Interaction Datasets

06.07.2026
We are pleased to announce that our project proposal, “Towards Realistic Keyboard, Mouse, and Touch User Studies: Creating Open-Access Datasets for Interaction Research,” has been selected for funding through the Institute of Computer Science’s Call for Project Ideas. Beginning in August 2026, this project addresses the need for high-quality, open-access datasets within the field of human-computer interaction. The project will focus on increasing the availability of open-access datasets to support scalable user-interaction research and to foster greater transparency and reproducibility.

Paper accepted at ICSME 2026

26.06.2026
We are happy to share that our Registered Report "Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues" by Emmanuel Charleson Dapaah, Philip Makedonski and Jens Grabowski has been accepted at the 42nd International Conference on Software Maintenance and Evolution (ICSME 2026). The conference will take place from 14 to 18 September 2026 in Benevento, Italy. This study investigates how training-dynamics patterns from class imbalance, overlap, and their coupling can be characterized under interaction-aware conditions in deep learning-based Software Defect Prediction.

Article published at Innovations in Systems and Software Engineering

26.06.2026
We are happy to announce that our article "From diagnosis to repair: A model-driven framework for root cause analysis of machine learning pipelines" is published online in the Journal of Innovations in Systems and Software Engineering. In the article, we propose a model-driven framework for root cause analysis and hyperparameter intervention that operates solely on structured descriptors. The framework uses three dataset-complexity meta-features, namely class overlap, class imbalance, and sparsity, together with learner hyperparameters. We evaluate the approach on two model families, Decision Trees (DT) and Multilayer Perceptrons (MLP). For each family, we construct a meta-dataset comprising 81,000 pipeline runs generated from 270 datasets and 300 hyperparameter configurations.

Funding for AI-generated code quality project

26.06.2026
We are pleased to announce that our proposal “A Reproducible Benchmark for AI-Generated vs. Human-Written Code: Comparing Quality and Test Demand” has been approved for funding through the Internal Call for Project Ideas 2026 at the Institute of Computer Science. The project will run in 2026 and aims to compare AI-generated and human-written code with respect to code quality and testing needs.

Paper accepted at EASE 2026

01.04.2026
We are happy to share that our paper Quality-Driven Selective Mutation for Deep Learning by Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski and Jens Grabowski has been accepted at the 30th International Conference on Evaluation and Assessment in Software Engineering (EASE 2026). The conference will take place from 9 to 12 June 2026 in Glasgow, United Kingdom. This study presents a probabilistic framework for quantifying mutant quality along two complementary dimensions: resistance and realism. The framework enables the ranking and filtering of low-quality mutation-operator configurations, substantially reducing mutation cost while preserving the practical value of mutants for testing and debugging.

Paper accepted at JAWs 2026

01.04.2026
We are pleased to announce that our paper titled Learning Model Mutations from Faults in Deep Learning by Zaheed Ahmed, Philip Makedonski and Jens Grabowski has been accepted for presentation at the Journal Ahead Workshop (JAWs 2026), part of the 48th International Conference on Software Engineering (ICSE 2026). As part of the acceptance, we received an invitation to submit an extended version of the paper to either ACM TOSEM or IEEE TSE within six months of the event. The conference will take place from 12 to 18 April 2026 in Rio de Janeiro, Brazil.

Paper accepted at HCII 2026

19.12.2025
We are pleased to announce that a paper from our research group has been accepted at the 28th International Conference on Human-Computer Interaction (HCII 2026). This year’s conference will take place from July 26th to 31st in Montreal, Canada. Our accepted paper is:
  • From Promise to Proof: Assessing Replicability and Reproducibility in Interaction-Based Emotion Recognition Research by Carina Bieber, Patrick Harms, and Jens Grabowski

Paper accepted at SAM 2025

02.10.2025

We are happy to announce that our paper Model-Driven Root Cause Analysis for Trustworthy AI: A Data-and-Model-Centric Explanation Framework (by Emmanuel Charleson Dapaah and Jens Grabowski) has been accepted at the The 17th System Analysis and Modelling conference (SAM 2025). This paper presents a model-driven Root Cause Analysis framework that explains ML pipeline performance by attributing outcomes to interpretable factors spanning both data complexity and model configuration. Paper accepted at SAM 2025.

Latest TDL specifications published

14.08.2025

ETSI’s Methods for Testing and Specification (MTS) committee has published an update of its TDL specifications. These specifications are:
The outcomes of our continued work on the Test Description Language (TDL) within TTF T034 of the European Telecommunications Standards Institute (ETSI) have been published as updated versions of the specifications:

You can find the complete series of TDL specifications here.

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