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
Static Code Features for a Machine Learning based Inspection: An approach for C
Blekinge Institute of Technology, School of Computing.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

Delivering fault free code is the clear goal of each devel- oper, however the best method to achieve this aim is still an open question. Despite that several approaches have been proposed in literature there exists no overall best way. One possible solution proposed recently is to combine static source code analysis with the discipline of machine learn- ing. An approach in this direction has been defined within this work, implemented as a prototype and validated subse- quently. It shows a possible translation of a piece of source code into a machine learning algorithm’s input and further- more its suitability for the task of fault detection. In the context of the present work two prototypes have been de- veloped to show the feasibility of the presented idea. The output they generated on open source projects has been collected and used to train and rank various machine learn- ing classifiers in terms of accuracy, false positive and false negative rates. The best among them have subsequently been validated again on an open source project. Out of the first study at least 6 classifiers including “MultiLayerPer- ceptron”, “Ibk” and “ADABoost” on a “BFTree” could convince. All except the latter, which failed completely, could be validated in the second study. Despite that the it is only a prototype, it shows the suitability of some machine learning algorithms for static source code analysis.

Place, publisher, year, edition, pages
2010. , 55 p.
Keyword [en]
static source code analysis, machine learning, feature selection, fault detection
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-2550Local ID: oai:bth.se:arkivexF8C05B338AD118EDC12577A6004B634BOAI: oai:DiVA.org:bth-2550DiVA: diva2:829833
Uppsok
Technology
Supervisors
Available from: 2015-04-22 Created: 2010-09-22 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(1372 kB)290 downloads
File information
File name FULLTEXT01.pdfFile size 1372 kBChecksum SHA-512
847cd05385a444b8d5d1ab5de23f31e620624eb5e35d8ef5d82793ec3a54cffbedd22d938854d5c74e2894c4a1e2ce6e181a3e53e54bdba6cc3af76c899392bf
Type fulltextMimetype application/pdf

By organisation
School of Computing
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 290 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: 45 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