data

description of data repository

analysis

info - a small info file

R

R scripts used to make the analisys, divided by folders:

• rq1_evolution - R scripts to perform analyses and plot for the evolution of CS
• rq1_relationship_cs - R scripts to perform analyses and plot for the relationsships of CS
• rq1_trends - R scripts to perform analyses and plot for the trends evolution of CS
• rq2_g_te - R scripts to perform analyses and plot for the causality of CS -granger causality and transfer entropy
• rq2_rel_groups - R scripts to perform analyses and plot for the relations inner goups of CS
• rq3_issues - R scripts to perform analyses and plot for the correlation and causality from CS to issues -correlations, linear regressions, granger causality(4 lags) and transfer entropy(4 lags) - individual CS and in group
• rq4_bugs - R scripts to perform analyses and plot for the correlation and causality from CS to bugs -correlations, linear regressions, granger causality(4 lags) and transfer entropy(4 lags) - individual CS and in group
• rq5_time_to_release - R scripts to perform analyses and plot for the correlation and causality from CS to time-to-telase - granger causality(4 lags) and transfer entropy(4 lags) - individual CS and in group

root: R includes ( to be included in the R analisys scripts)

Data folders used in the questions

rq1

data to calculate averages and trends

rq2

data of correlations, linear regression, granger causality(4 lags) and transfer entropy(4 lags) inter CS groups

rq3

data of correlations, linear regression, granger causality(4 lags) and transfer entropy(4 lags) from CS to issues, individual and in group, divided by 8 folders:

• data (values of CS density vs issues)

• data_issuesn (values of CS density vs issues density)

• lm linear regression - values and statistical significance - separated and grouped

• te(te lag1) to te lag4) transfer entropy values and statistical significance - separated and grouped

• data_corts - time series correlation (CS density vs issues, no agregation)

• data_corts_absolute - time series correlation (absolute CS vs issues, no agregation)

• data_granger - granger causality (lags 1 to 4) - CS density vs issues absolute

• data_grangernnn - granger causality (lags 1 to 4) - CS density vs issues density

CS groups:

• data_scope (values of CS groups density vs issues - lm and TE - 4 lags - statistical significance and TE values)
• data_issuesn_scope (values of CS goups density vs issues density - lm and TE - 4 lags - statistical significance and TE values)

rq4

the same as rq3 folders but for bugs

rq5

the same as rq3 minus the correlations

data folders

Aggregation of data, each app has two files, CSV and xls, with lines for each released official version and columns for the variable:. Id;Version;Date;Age;VersionAge; CyclomaticComplexity; NPathComplexity;ExcessiveMethodLength;ExcessiveClassLength;ExcessiveParameterList;ExcessivePublicCount;TooManyFields;TooManyMethods;TooManyPublicMethods;ExcessiveClassComplexity;NumberOfChildren;DepthOfInheritance;CouplingBetweenObjects;DevelopmentCodeFragment;UnusedPrivateField;UnusedLocalVariable;UnusedPrivateMethod;UnusedFormalParameter; embed JS;inline JS;embed CSS;inline CSS;css in JS;css in JS: jquery; max-lines;max-lines-per-function;max-params;complexity;max-depth;max-nested-callbacks;PHP;HTML;JavaScript;CSS;HTML_template;HTML_diff;Client;SUM

data_sep

5 files for each app, with lines corresponding to the each released official version.

serversmells

by app and release , columns;. Id;Version;Date; CyclomaticComplexity; NPathComplexity;ExcessiveMethodLength;ExcessiveClassLength;ExcessiveParameterList;ExcessivePublicCount;TooManyFields;TooManyMethods;TooManyPublicMethods;ExcessiveClassComplexity;NumberOfChildren;DepthOfInheritance;CouplingBetweenObjects;DevelopmentCodeFragment;UnusedPrivateField;UnusedLocalVariable;UnusedPrivateMethod;UnusedFormalParameter

clientsmells

by app and release , columns;. Id;Version;Date; embed JS;inline JS;embed CSS;inline CSS;css in JS;css in JS: jquery

jssmells

by app and release , columns;. Id;Version;Date; max-lines;max-lines-per-function;max-params;complexity;max-depth;max-nested-callbacks

Cloc(metrics)

by app and release , columns;. "id";"version";"PHP";"HTML";"JavaScript";"CSS";"HTML_template";"HTML_diff";"Client";"SUM"

version

by app and release , columns;. "id";"version";"date"

issues_bugs

3 folders

data

issues by app by release (lines/records) with columns:. id;version1;version2;date1;date2;count

data_bugs_more

the same but only bugs, by app by release (lines/records) with columns:. id;version1;version2;date1;date2;count

scripts

scrips used to aggregate issues (from daily issues to by release) anf the same for bugs