BugSeverityPrediction | Predicting Severity of Bug Reports
kandi X-RAY | BugSeverityPrediction Summary
kandi X-RAY | BugSeverityPrediction Summary
BugSeverityPrediction is a Java library. BugSeverityPrediction has no bugs, it has no vulnerabilities and it has low support. However BugSeverityPrediction build file is not available. You can download it from GitHub.
This artifact is to replicate "Predicting the Severity of a Reported Bug" by Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger and Bart Goethals and originally presented in MSR 2010.
This artifact is to replicate "Predicting the Severity of a Reported Bug" by Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger and Bart Goethals and originally presented in MSR 2010.
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Quality
Security
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Support
BugSeverityPrediction has a low active ecosystem.
It has 1 star(s) with 2 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
BugSeverityPrediction has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of BugSeverityPrediction is current.
Quality
BugSeverityPrediction has no bugs reported.
Security
BugSeverityPrediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
BugSeverityPrediction does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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BugSeverityPrediction releases are not available. You will need to build from source code and install.
BugSeverityPrediction has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed BugSeverityPrediction and discovered the below as its top functions. This is intended to give you an instant insight into BugSeverityPrediction implemented functionality, and help decide if they suit your requirements.
- Runs the ARFF report
- Load the severity labels for a report file
- Load the description from a report file
- Load the report IDs from the report file
- Build a dictionary
- Extract vector from a dictionary
- Main entry point
- Shows the actual prediction result
- Split the cross validation
- Loads a configuration from a file
- Classifies the given modelifier
- Main method for testing
- Calculate accuracy for the given data vector
- Read a data file
Get all kandi verified functions for this library.
BugSeverityPrediction Key Features
No Key Features are available at this moment for BugSeverityPrediction.
BugSeverityPrediction Examples and Code Snippets
No Code Snippets are available at this moment for BugSeverityPrediction.
Community Discussions
No Community Discussions are available at this moment for BugSeverityPrediction.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install BugSeverityPrediction
You can download it from GitHub.
You can use BugSeverityPrediction like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the BugSeverityPrediction component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
You can use BugSeverityPrediction like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the BugSeverityPrediction component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
Support
This artifact is to replicate "Predicting the Severity of a Reported Bug" by Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger and Bart Goethals and originally presented in MSR 2010.
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