A Stochastic Approach Based on Rational Decision-Making for Analyzing Software Engineering Project Status
This study presents a novel approach to project status prediction in software engineering, based on unobservable states of decision-making processes, utilizing Hidden Markov Models (HMMs). By establishing HMM structures and leveraging the Rational Decision Making model (RDM), we encoded underlying project conditions; observed project data from a software engineering organization were utilized to estimate model parameters via the Baum-Welch algorithm. The developed HMMs, four project-specific models, were subsequently tested with empirical data, demonstrating their predictive potential. However, a generalized, aggregated model did not show any sufficient accuracy. Model development and experiments were made in Python. Our approach presents preliminary work and a pathway for understanding and forecasting project dynamics in software development environments.
Tue 12 DecDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
14:00 - 15:00 | Machine Learning and Data ScienceResearch Papers / Organization / Short Papers and Posters / Industry Papers at W211 Chair(s): Xiaozhou Li University of Oulu | ||
14:00 10mShort-paper | Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning Short Papers and Posters Lisa Jöckel Fraunhofer, Michael Klaes Fraunhofer IESE; Konstanz University of Applied Sciences, Janek Groß Fraunhofer IESE, Pascal Gerber Fraunhofer Institute for Experimental Software Engineering, Markus Scholz NovelSense, Jonathan Eberle TRUMPF, Marc Teschner TRUMPF, Daniel Seifert Fraunhofer IESE, Richard Hawkins University of York, John Molloy University of York, Jens Ottnad TRUMPF | ||
14:10 10mResearch paper | Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey Research Papers Antonio Pedro Santos Alves Pontifical Catholic University of Rio de Janeiro, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Görkem Giray Independent Researcher, Daniel Mendez Blekinge Institute of Technology, Niklas Lavesson Blekinge Institute of Technology, Kelly Azevedo Pontifical Catholic University of Rio de Janeiro, Hugo Villamizar Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Tatiana Escovedo Pontifical Catholic University of Rio de Janeiro, Helio Côrtes Vieira Lopes PUC-Rio, Stefan Biffl Vienna University of Technology, Juergen Musil , Michael Felderer German Aerospace Center (DLR) & University of Cologne, Stefan Wagner University of Stuttgart, Maria Teresa Baldassarre Department of Computer Science, University of Bari , Tony Gorschek Blekinge Institute of Technology / DocEngineering | ||
14:20 10mShort-paper | A Stochastic Approach Based on Rational Decision-Making for Analyzing Software Engineering Project Status Short Papers and Posters Hannes Salin Dalarna University | ||
14:30 10mResearch paper | CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems Research Papers | ||
14:40 10mShort-paper | Comparing machine learning algorithms for medical time-series data Short Papers and Posters Aliasgar Shereef Chalmers University of Technology, Alex Helmersson Chalmers University of Technology, Faton Hoti Chalmers University of Technology, Sebastian Levander Gothenburg University, Emil Svensson Chalmers University of Technology, Ali El-Merhi Sahlgrenska University Hospital, Richard Vithal Sahlgrenska University Hospital, Jaquette Liljenqrantz Sahlgrenska University Hospital, Linda Block Sahlgrenska University Hospital , Helena Odenstedt Sahlgrenska University Hospital, Miroslaw Staron Chalmers University of Technology | ||
14:50 10mResearch paper | What Data Scientists (Care to) Recall Research Papers Samar Saeed University of Gothenburg, Shahrzad Sheikholeslami University of Gothenburg, Jacob Krüger Eindhoven University of Technology, Regina Hebig University of Rostock | ||