Area: Convergence of Software Engineering, Data Science, and Experimental Methods, with a focus on Generative Software Engineering (AI-driven software development, software recommendation and behavior-aware analysis and comparison)

Research Interests

• Program Analysis
  • Scalable, Synergetic Static and Dynamic Analysis (Observation)
  • Software Testing and Quality
• Empirical Software Engineering and Measurement
  • Test-driven Software Experimentation
• Mining Software Repositories (“Big Code”)
  • Code Retrieval & Recommendation (Software Reuse)
  • Software Analytics
• AI-powered Software (“AIWare”)
  • Generative Artificial Intelligence in Software Engineering Tasks

In recent years ...

• FSS25 
  • CS 630 – Generative Software Engineering (Master)
• HWS24
  • CS 500 – Advanced Software Engineering (Testing & Experimentation) (Master)
• FSS24
  • CS 306 – Praktikum Software Engineering (Bachelor)
  • Big Data Engineering and Analysis (Hochschule Mannheim)
• HWS23
  • CS 500 – Advanced Software Engineering (Master)
  • DHBW Mannheim (visiting lecturer): Advanced Software Engineering
• FSS23
  • CS 470 – Python for Data Scientists (Master)
  • CS 308 – Softwaretechnik I (Bachelor)
  • Hochschule Mannheim (visiting lecturer): Big Data Engineering and Analysis
• HWS22
  • CS 500 – Advanced Software Engineering (Master)
• FSS22
  • CS 470 – Python for Data Scientists (Master)
  • Hochschule Mannheim (visiting lecturer): Big Data Engineering and Analysis
• regular: Bachelor/Master Theses, Master Team Projects, Individual Projects
• ...

LASSO – A Large-Scale Software Observatorium for the Dynamic Selection, Analysis and Comparison of Software

LASSO's platform enables scalable software code analysis and observation of big code. It provides mass analysis of sofware code combining dynamic and static program analysis techniques to observe functional (i.e., behavioural) and non-functional properties about software code. It is primarily used to conduct active research in software engineering (contact us), but can also be used by practitioners.

Based on these capabilities, LASSO can be used to realize reusable code analysis services using a dedicated pipeline language, LSL (LASSO Scripting Language). This includes services like -

• code search and retrieval (interface-driven code search, test-driven code search)
• N-version assessment based on alternative implementations either retrieved via code search or generated using generative AI
• automated (unit) test generation
• test-driven software experimentation as a service
• benchmarking of tools/techniques
• ...

(excerpt from https://softwareobservatorium.github.io/web/about)

The LASSO project (Software Observatorium) is hosted on GitHub including documentation, live demo of its analysis services etc. -

• https://softwareobservatorium.github.io/ 

Research Seed Capital (RiSC), Ministerium für Wissenschaft, Forschung und Kunst Baden Württemberg

• Title: Automated Test Oracle Recommendation – AUTOR
• Description: Harvesting test oracle information using the LASSO platform and generative artificial intelligence to recommend test oracles
• Area of Research: Software Testing (Oracle Problem)
• Duration: 2 years
• Start: September 2023

Open Science Grants 2024, University of Mannheim

• Title: TDSE-Hub: A Repository for Reproducible Test-driven Software Experiments
• Description: A repository for reproducible experiments in test-driven software experimentation
• Area of Research: Test-driven software experimentation
• Duration: 1 year
• Start: September 2024

Karin Islinger Prize

• Description: Dissertation prize awarded by the Karin Islinger Stiftung

List of Publications

• Scholar
• ORCID
• madoc

Dissertation

• Title: LASSO – an observatorium for the dynamic selection, analysis and comparison of software
• available here

Social Media

• Linkedin
• Researchgate