Data
ar6

ar6

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • mythbusting_1 study_1 study_123 study_15 study_20 study_41 study_52 study_7 study_88
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Author: Source: Unknown - Date unknown Please cite: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable, refutable, verifiable, and/or improvable predictive models of software engineering. If you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on the PROMISE repository web page http://promise.site.uottowa.ca/SERepository. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% --Title: AR6 /Software Defect Prediction --Date: February, 4th, 2009 --Data from a Turkish white-goods manufacturer --Donated by: Software Research Laboratory (Softlab), Bogazici University, Istanbul, Turkey --Website: http://softlab.boun.edu.tr --Contact address: ayse.tosun@boun.edu.tr, bener@boun.edu.tr --Description: Embedded software in a white-goods product. Implemented in C. Consists of 101 modules (15 defective / 86 defect-free) 29 static code attributes (McCabe, Halstead and LOC measures) and 1 defect information(false/true) Function/method level static code attributes are collected using Prest Metrics Extraction and Analysis Tool [1]. [1] Prest Metrics Extraction and Analysis Tool, available at http://softlab.boun.edu.tr/?q=resources&i=tools.

30 features

defects (target)nominal2 unique values
0 missing
total_locnumeric43 unique values
0 missing
blank_locnumeric7 unique values
0 missing
comment_locnumeric21 unique values
0 missing
code_and_comment_locnumeric5 unique values
0 missing
executable_locnumeric36 unique values
0 missing
unique_operandsnumeric32 unique values
0 missing
unique_operatorsnumeric19 unique values
0 missing
total_operandsnumeric50 unique values
0 missing
total_operatorsnumeric55 unique values
0 missing
halstead_vocabularynumeric41 unique values
0 missing
halstead_lengthnumeric66 unique values
0 missing
halstead_volumenumeric79 unique values
0 missing
halstead_levelnumeric40 unique values
0 missing
halstead_difficultynumeric39 unique values
0 missing
halstead_effortnumeric87 unique values
0 missing
halstead_errornumeric25 unique values
0 missing
halstead_timenumeric88 unique values
0 missing
branch_countnumeric20 unique values
0 missing
decision_countnumeric19 unique values
0 missing
call_pairsnumeric14 unique values
0 missing
condition_countnumeric17 unique values
0 missing
multiple_condition_countnumeric10 unique values
0 missing
cyclomatic_complexitynumeric15 unique values
0 missing
cyclomatic_densitynumeric34 unique values
0 missing
decision_densitynumeric15 unique values
0 missing
design_complexitynumeric16 unique values
0 missing
design_densitynumeric30 unique values
0 missing
normalized_cyclomatic_complexitynumeric28 unique values
0 missing
formal_parametersnumeric4 unique values
0 missing

19 properties

101
Number of instances (rows) of the dataset.
30
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
29
Number of numeric attributes.
1
Number of nominal attributes.
85.15
Percentage of instances belonging to the most frequent class.
3.33
Percentage of nominal attributes.
86
Number of instances belonging to the most frequent class.
14.85
Percentage of instances belonging to the least frequent class.
15
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
3.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.82
Average class difference between consecutive instances.
0
Percentage of missing values.
0.3
Number of attributes divided by the number of instances.
96.67
Percentage of numeric attributes.

5 tasks

596 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
206 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: defects
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