Improving open source software process quality based on defect data mining

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 activities. 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. However, 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.

Original languageEnglish
Title of host publicationSWQD
Subtitle of host publicationProcess Automation in Software Development - 4th International Conference, SWQD 2012, Proceedings
PublisherSpringer
Pages84 – 102
Number of pages19
Volume94 LNBIP
ISBN (Print)9783642272127
DOIs
Publication statusPublished - 19 Jan 2012
Externally publishedYes

Publication series

NameLecture Notes in Business Information Processing
Volume94 LNBIP
ISSN (Print)1865-1348

Keywords

  • Data mining
  • Defects
  • Managers
  • Model checking
  • Open systems
  • Project management
  • Quality control
  • Software design
  • Correlation analysis
  • Defect prediction
  • Key indicator
  • Mining algorithms
  • Open Source Software
  • Open source software projects
  • Process metrics
  • Process model
  • Process Quality
  • Product quality
  • Project developers
  • Project managers
  • Computer software selection and evaluation
  • Data Mining

Research fields

  • Software Product Lines
  • Process Innovation
  • Digital Transformation

IMC Research Focuses

  • Software engineering and intelligent systems

ÖFOS 2012 - Austrian Fields of Study

  • 102035 Data science
  • 102028 Knowledge engineering

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