By the same authors

Developer-Centric Knowledge Mining from Large Open-Source Software Repositories (CROSSMINER)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published copy (DOI)

Author(s)

  • Alessandra Bagnato
  • Konstantinos Barmpis
  • Nik Bessis
  • Luis Adrián Cabrera-Diego
  • Juri Di Rocco
  • Davide Di Ruscio
  • Tamás Gergely
  • Scott Hansen
  • Dimitris Kolovos
  • Philippe Krief
  • Ioannis Korkontzelos
  • Stéphane Laurière
  • Jose Manrique Lopez de la Fuente
  • Pedro Maló
  • Richard F. Paige
  • Diomidis Spinellis
  • Cedric Thomas
  • Jurgen J. Vinju

Department/unit(s)

Publication details

Title of host publicationSoftware Technologies: Applications and Foundations - STAF 2017 Collocated Workshops, Revised Selected Papers
DatePublished - 2018
Pages375-384
Number of pages10
PublisherSpringer-Verlag
Volume10748 LNCS
Original languageEnglish
ISBN (Print)9783319747293

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10748 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Abstract

Deciding if an OSS project meets the required standards for adoption is hard, and keeping up-to-date with a rapidly evolving project is even harder. Making decisions about quality and adoption involves analysing code, documentation, online discussions, and issue trackers. There is too much information to process manually and it is common that uninformed decisions have to be made with detrimental effects. CROSSMINER aims to remedy this by automatically extracting the required knowledge and injecting it into the developers’ Integrated Development Environments (IDE), at the time they need it to make design decisions. This allows them to reduce their effort in knowledge acquisition and to increase the quality of their code. CROSSMINER uniquely combines advanced software project analyses with online IDE monitoring. Developers will be monitored to infer which information is timely, based on readily available knowledge stored earlier by a set of advanced offline deep analyses of related OSS projects.

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