KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/44526
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | K.K. Chaturvedi | en_US |
dc.contributor.author | V.B. Singh | en_US |
dc.date.accessioned | 2021-01-04T07:21:17Z | - |
dc.date.available | 2021-01-04T07:21:17Z | - |
dc.date.issued | 2014-02-17 | - |
dc.identifier.citation | Chaturvedi, KK and Singh, VB(2013). Bug prediction using entropy based measures. International Journal Knowledge Engineering and Data Mining, 2(4): 266-291. | en_US |
dc.identifier.uri | https:/doi.org/10.1504/IJKEDM.2013.059319 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/44526 | - |
dc.description | Not Available | en_US |
dc.description.abstract | In the available literature, researchers have proposed and implemented a plethora of bug prediction approaches, which vary in terms of accuracy, complexity and the input data they require, but very few of them has predicted the number of bugs in the software based on the entropy or the complexity of code changes. To use the entropy of code change as a bug predictor, firstly, the history of complexity metric (HCM) defined with different decay weight and decay models were assigned to it (Hassan, 2009). But, they did not propose any method to find out the value of decay rate/factor. In this paper, we proposed a new weight to HCM, a method to find out the value of decay rate/factor and proposed some novel decay-based methods. We have applied simple linear regression (SLR) and support vector regression (SVR) to predict the bugs based on existing and proposed methods of HCM. We have also studied the performance of different complexity of code changes (entropy)-based bug prediction approaches on the basis of various performance measures using four subsystems of Mozilla project. We found that decay models for SVR show better results in comparison with SLR. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Inderscience Publishers | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | bug prediction | en_US |
dc.subject | entropy | en_US |
dc.subject | software versioning system | en_US |
dc.subject | software repository | en_US |
dc.subject | code change complexity | en_US |
dc.subject | software bugs | en_US |
dc.subject | simple linear regression | en_US |
dc.subject | support vector regression | en_US |
dc.subject | decay weight | en_US |
dc.subject | decay models | en_US |
dc.subject | performance measures | en_US |
dc.subject | software development | en_US |
dc.title | Bug prediction using entropy-based measures | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | International Journal of Knowledge Engineering and Data Mining | en_US |
dc.publication.volumeno | 2 | en_US |
dc.publication.pagenumber | 266 - 291 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https:/doi.org/10.1504/IJKEDM.2013.059319 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | Not Available | - |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
There are no files associated with this item.
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.