Prof. Dr. Holger Giese

Christian Medeiros Adriano (Chris)

Telefon: +49 331 5509 319

E-Mail: christian.adriano@hpi.de

Raum:  A-2.7

External page: linked-in 


>2019.06.19 | Talk:Enabling a crowd of programmers to work in parallel to identify, explain, and fix software bugs. Location: Meeting of the German Computer Science Research Training Groups 2019 in Dagstuhl
>2019.04.09 | Talk:Tackling the Perfect Fault Understanding Assumption with One Thousand Programmers in the Loop . Location: FutureSoc Symposium
>2018.07.18 | Talk: "Microtasking Fault Localization". Location: Doctoral Symposium at the Empirical Software Engineering Conference.
>2018.07.02 | Talk: "Crowdsourcing the localization and fixing of software faults". Location: Software Engineering Group at the Humbolt University.
>2018.06.15 | Talk: "Learning Utility-changes for Rule-based Adaptation of Dynamic Architectures - Current and Future work". (with Sona Ghahremani). Location:  Analysis & Modelling Group Seminar.
>2018.03.09 | Talk: "Can a crowd identify the cause of a software failure and suggest valid bug fixes?" LocationUniversity of Cape Town

Research interests

A recurrent theme in my research is how to build statistical models to predict and explain task outcomes, which are essentially subjective opinions about a software failure. My models help me investigate how to aggregate conflicting opinions, decide if more opinions are necessary about the same item or different items, and who should we ask for further opinions.

While pursuing these research problems, I rely on a set of models and technology:

  • Theory: Decision Theory (e.g., voting, utility, prospect theory)
  • Probabilistic Models: Causal Inference, Bayesian Inference, Markov models and Bayesian Optimization
  • Empirical research: Quasi-experimental and Observational studies, Survival analysis, Analyses of covariance, and Sensitivity analysis
  • Application domains: Crowdsourcing, Software Debugging, Self-Adaptive Systems, and, more recently, Comments from Q&A sites or Newspapers
  • Tools/Languages: R, Python, Graph Database, Spark



My Google Scholar - link