Digitala Vetenskapliga Arkivet

Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automatic Dispatching of Issues using Machine Learning
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk fördelning av ärenden genom maskininlärning (Swedish)
Abstract [en]

Many software companies use issue tracking systems to organize their work. However, when working on large projects, across multiple teams, a problem of finding the correctteam to solve a certain issue arises. One team might detect a problem, which must be solved by another team. This can take time from employees tasked with finding the correct team and automating the dispatching of these issues can have large benefits for the company. In this thesis, the use of machine learning methods, mainly convolutional neural networks (CNN) for text classification, has been applied to this problem. For natural language processing both word- and character-level representations are commonly used. The results in this thesis suggests that the CNN learns different information based on whether word- or character-level representation is used. Furthermore, it was concluded that the CNN models performed on similar levels as the classical Support Vector Machine for this task. When compared to a human expert, working with dispatching issues, the best CNN model performed on a similar level when given the same information. The high throughput of a computer model, therefore, suggests automation of this task is very much possible.

Place, publisher, year, edition, pages
2019. , p. 90
Keywords [en]
NLP, Machine Learning, Convolutional Neural Networks, CNN, SVM, bug report, issue, text classification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-162837ISRN: LIU-IDA/LITH-EX-A--19/043--SEOAI: oai:DiVA.org:liu-162837DiVA, id: diva2:1386476
External cooperation
anonym
Subject / course
Computer science
Presentation
2019-06-11, 13:15
Supervisors
Examiners
Available from: 2020-05-20 Created: 2020-01-17 Last updated: 2020-05-20Bibliographically approved

Open Access in DiVA

Automatic Dispatching of Issues using Machine Learning(1442 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 1442 kBChecksum SHA-512
3e1e4a0db4f804209f9f7fa487fb7291140e99d55d7b1041063d23c61b56c279b96da733b3b36c686b5b81cb1bfd36bbcf49a0c48d0a41f1117750ef7fbdcff9
Type fulltextMimetype application/pdf

By organisation
Software and SystemsFaculty of Science & Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf