dc.contributor.advisor |
Lingras, Pawan |
|
dc.creator |
MacDonald, Ross Earle |
|
dc.date.accessioned |
2018-10-18T14:04:49Z |
|
dc.date.available |
2018-10-18T14:04:49Z |
|
dc.date.issued |
2018 |
|
dc.identifier.other |
QA76.76 D47 M33 2018 |
|
dc.identifier.uri |
http://library2.smu.ca/handle/01/28113 |
|
dc.description |
viii, 125 leaves : illustrations ; 29 cm |
|
dc.description |
Includes abstract and appendices. |
|
dc.description |
Includes bibliographical references (leaves 114-117). |
|
dc.description.abstract |
Improvement in software development practices to predict and reduce software defects can lead to major cost savings. The goal of this thesis is to demonstrate the value of static analysis metrics and rules in predicting software defects at a much larger scale than previous efforts. The study analyses data collected from more than 500 software applications, across 3 multi-year software development programs, and uses over 150 software static analysis measurements. Static analysis metrics, rule violations and software defect historical actual values are sourced from multiple disparate databases, joined and groomed for analysis. Several feature selection techniques are employed to narrow the feature set focus to the most influential variables. Furthermore, a number of machine learning techniques such as neural network and random forest are used to determine whether seemingly innocuous rule violations can be used as significant predictors of software defect rates. |
en_CA |
dc.description.provenance |
Submitted by Greg Hilliard (greg.hilliard@smu.ca) on 2018-10-18T14:04:48Z
No. of bitstreams: 1
MacDonald_Ross_MASTERS_2018.pdf: 3076033 bytes, checksum: a38c7139a7b139bac3c635a71a356631 (MD5) |
en |
dc.description.provenance |
Made available in DSpace on 2018-10-18T14:04:49Z (GMT). No. of bitstreams: 1
MacDonald_Ross_MASTERS_2018.pdf: 3076033 bytes, checksum: a38c7139a7b139bac3c635a71a356631 (MD5)
Previous issue date: 2018-08-22 |
en |
dc.language.iso |
en |
en_CA |
dc.publisher |
Halifax, N.S. : Saint Mary's University |
|
dc.subject.lcc |
QA76.76.D47 |
|
dc.subject.lcsh |
Computer software -- Development |
|
dc.subject.lcsh |
Machine learning |
|
dc.title |
Software defect prediction from code quality measurements via machine learning |
en_CA |
dc.type |
Text |
en_CA |
thesis.degree.name |
Master of Science in Computing and Data Analytics |
|
thesis.degree.level |
Masters |
|
thesis.degree.discipline |
Mathematics and Computing Science |
|
thesis.degree.grantor |
Saint Mary's University (Halifax, N.S.) |
|