Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

W. Sunindyo, T Moser, D. Winkler, D. Dhungana:
"Improving Open Source Software Process Quality Based on Defect Data Mining";
Vortrag: Software Quality Days 2012, Vienna, Austria; 17.01.2012 - 19.01.2012; in: "Proceedings of Software Quality Days 2012, Research Track", (2012), S. 84 - 102.

Kurzfassung englisch:
Open Source Software (OSS) project managers often need to observe project key indicators, e.g., how much efforts are needed to finish certain tasks, to assess and improve project and product quality, e.g., by analyzing defect data from OSS project developer activi-ties. Previous work was based on analyzing defect data of OSS projects by using correlation analysis approach for defect prediction on a combination of product and process metrics. How-ever, this correlation analysis is focusing on the relationship between two variables without exploring the characterization of that relationship. We propose an observation framework that explores the relationship of OSS defect metrics by using data mining approach (heuristics mining algorithm). Major results show that our framework can support OSS project managers in observing project key indicators, e.g., by checking conformance between the designed and actual process models.

Open Source Software, Process Quality, Data Mining

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.