Categorizing Software Defects using Machine Learning
(2018) In LU-CS-EX 2018-17 EDAM05 20181Department of Computer Science
- Abstract
- We analyze how automatically generated crash reports can be used to aid in
the process of software defect categorization. The crash reports are automatically
generated logs, which vary widely in both format and in information
content. Each crash report used is linked to its corresponding, human-written
bug report.
The problem is handled as a long text-based classification problem. Several
different machine learning techniques are compared. Amongst these is our
own Keras-based implementation of a Hierarchical Attention Network, with
which we achieved an accuracy of 72.5% on Severity prediction, and an accuracy
of 51.4% on Responsible Group prediction.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8964339
- author
- Stagge, Viktor LU
- supervisor
-
- Markus Borg LU
- organization
- course
- EDAM05 20181
- year
- 2018
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- crash reports, software defects, text classification, hierarchical attention networks, machine learning
- publication/series
- LU-CS-EX 2018-17
- report number
- LU-CS-EX 2018-17
- ISSN
- 1650-2884
- language
- English
- id
- 8964339
- date added to LUP
- 2018-12-19 13:38:14
- date last changed
- 2018-12-19 13:38:14
@misc{8964339, abstract = {{We analyze how automatically generated crash reports can be used to aid in the process of software defect categorization. The crash reports are automatically generated logs, which vary widely in both format and in information content. Each crash report used is linked to its corresponding, human-written bug report. The problem is handled as a long text-based classification problem. Several different machine learning techniques are compared. Amongst these is our own Keras-based implementation of a Hierarchical Attention Network, with which we achieved an accuracy of 72.5% on Severity prediction, and an accuracy of 51.4% on Responsible Group prediction.}}, author = {{Stagge, Viktor}}, issn = {{1650-2884}}, language = {{eng}}, note = {{Student Paper}}, series = {{LU-CS-EX 2018-17}}, title = {{Categorizing Software Defects using Machine Learning}}, year = {{2018}}, }