handel senti
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266
4_rq3.tex
266
4_rq3.tex
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@ -5,7 +5,7 @@
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\label{rq3}
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In order to investigate what kind of crowd duplications are more likely to be accepted,
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% we conduct manual inspection and regression analysis.
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% \subsubsection{Manual inspection.}
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\subsubsection{Manual inspection.}
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we manually explore the decision elaborations made by reviewers
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when they make a choice between duplications.
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This work is conducted on 100 pairs~\footnote{\url{https://github.com/whystar/dup_pr_cmt/blob/master/README.md}}
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@ -232,177 +232,177 @@ closing in favour of \#10582
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% #########################################
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% ############ 2019-01-12注释掉 ############
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% % work pattern
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% \subsubsection{Regression analysis.}
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% Furthermore,
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% we would like to explore whether there are some other metrics
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% that are not explicitly mentioned by the reviewers
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% but do have an effect the duplication competition.
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% Based on our research experience and the review of prior related studies,
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% we collect the following metrics from three different perspectives.
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% work pattern
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\subsubsection{Regression analysis.}
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Furthermore,
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we would like to explore whether there are some other metrics
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that are not explicitly mentioned by the reviewers
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but do have an effect the duplication competition.
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Based on our research experience and the review of prior related studies,
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we collect the following metrics from three different perspectives.
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% % 贡献者和审阅者对文件的掌控力，修改次数，历史touch数
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% 贡献者和审阅者对文件的掌控力，修改次数，历史touch数
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% % 回归分析
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% \vspace{0.5em}
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% \noindent\textbf{Contributor level metrics.}
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% 回归分析
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\vspace{0.5em}
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\noindent\textbf{Contributor level metrics.}
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% Contributor level metrics are used to represent the role and contribution experience
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% of developers,
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% which includes the following items.
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% % 如何获得这些特征 等等
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Contributor level metrics are used to represent the role and contribution experience
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of developers,
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which includes the following items.
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% 如何获得这些特征 等等
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% \textit{Core\_team.}
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% % 表征角色
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% This is a binary metric,
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% indicating whether a contributor is the member of the core management team of the project.
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% Specifically in the paper,
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% the developers who have the privilege to close others' pull-request
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% form the core management team.
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\textit{Core\_team.}
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% 表征角色
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This is a binary metric,
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indicating whether a contributor is the member of the core management team of the project.
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Specifically in the paper,
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the developers who have the privilege to close others' pull-request
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form the core management team.
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% \textit{Submission\_exp.}
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% % 表征经验
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% This metric measures a contributor's prior submission experience,
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% which is computed by counting the number of prior pull-requests and issues
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% submitted by the contributor to a specific project
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% before the contribution.
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\textit{Submission\_exp.}
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% 表征经验
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This metric measures a contributor's prior submission experience,
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which is computed by counting the number of prior pull-requests and issues
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submitted by the contributor to a specific project
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before the contribution.
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% \textit{Discussion\_exp.}
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% % 表征经验
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% Similar to $Submission\_exp$,
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% this metric measures a contributor's prior discussion experience
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% and it indicates the number of prior comments that the contributor has made
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% on the pull-requests and issues of a specific project
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% before this contribution.
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\textit{Discussion\_exp.}
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% 表征经验
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Similar to $Submission\_exp$,
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this metric measures a contributor's prior discussion experience
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and it indicates the number of prior comments that the contributor has made
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on the pull-requests and issues of a specific project
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before this contribution.
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% \vspace{0.5em}
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% \noindent\textbf{Pull-request level metrics.}
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\vspace{0.5em}
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\noindent\textbf{Pull-request level metrics.}
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% Pull-request level metrics represent the main profile and change information
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% of a pull-request, which includes the following items.
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Pull-request level metrics represent the main profile and change information
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of a pull-request, which includes the following items.
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% \textit{First\_pr.}
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% % 表征新手
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% This is a binary metric which indicates
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% whether this is the first time for a contributor to submit a pull-request
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% to a specific project.
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\textit{First\_pr.}
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% 表征新手
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This is a binary metric which indicates
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whether this is the first time for a contributor to submit a pull-request
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to a specific project.
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% \textit{Early\_arrival.}
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% This binary metric is used to indicate whether
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% a pull-request is submitted early than the other one for a pair of duplications.
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% Therefore,
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% the metric $Submitted\_early$ of the early submitted pull-request is set to be $True$,
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% while for the pull-request with late submission time the metric value is $False$.
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\textit{Early\_arrival.}
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This binary metric is used to indicate whether
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a pull-request is submitted early than the other one for a pair of duplications.
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Therefore,
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the metric $Submitted\_early$ of the early submitted pull-request is set to be $True$,
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while for the pull-request with late submission time the metric value is $False$.
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% \textit{Churn.}
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% This metric represents the total number of lines of code
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% that a pull-requests has changed (\ie added, removed and modified).
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\textit{Churn.}
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This metric represents the total number of lines of code
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that a pull-requests has changed (\ie added, removed and modified).
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% \textit{\#Files.}
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% This metric indicates the total number of the files
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% that a pull-requests has changed (\ie created, deleted and modified).
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\textit{\#Files.}
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This metric indicates the total number of the files
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that a pull-requests has changed (\ie created, deleted and modified).
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% % \textit{Test\_touched.}
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% % This is a binary metric which represents
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% % whether a pull-request has changed the test files.
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% \textit{Test\_touched.}
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% This is a binary metric which represents
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% whether a pull-request has changed the test files.
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% \vspace{0.5em}
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% \noindent\textbf{Review level metrics.}
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\vspace{0.5em}
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\noindent\textbf{Review level metrics.}
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% Review level metrics mainly inflect the information
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% contained in the code review process of a pull-request,
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% which includes the following items.
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Review level metrics mainly inflect the information
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contained in the code review process of a pull-request,
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which includes the following items.
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% \textit{Review\_duration.}
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% This metric computes the review duration of a pull-request
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% from the time it is submitted
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% to the duplication exposure time when its duplicate relation with another one is detected.
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\textit{Review\_duration.}
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This metric computes the review duration of a pull-request
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from the time it is submitted
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to the duplication exposure time when its duplicate relation with another one is detected.
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% \textit{\#Comments.}
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% This metric indicates the total number of review comments
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% a pull-request has received until the duplication exposure time.
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\textit{\#Comments.}
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This metric indicates the total number of review comments
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a pull-request has received until the duplication exposure time.
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% \textit{\#Comments\_inline.}
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% This metric indicates the total number of inline comments
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% a pull-request has received until the duplication exposure time.
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% Actually,
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% reviewers can leave two kinds of review comments on a pull-request,
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% \ie \textit{inline comments} which mainly focus on solution details
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% and \textit{issue comments} which usually talk about the general issues of the whole pull-request~\cite{Zhi2017What}.
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\textit{\#Comments\_inline.}
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This metric indicates the total number of inline comments
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a pull-request has received until the duplication exposure time.
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Actually,
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reviewers can leave two kinds of review comments on a pull-request,
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\ie \textit{inline comments} which mainly focus on solution details
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and \textit{issue comments} which usually talk about the general issues of the whole pull-request~\cite{Zhi2017What}.
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% \begin{table*}[h]
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% \centering
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% \caption{Overview of metrics}
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% \begin{tabular}{r c c c c c c c}
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\begin{table*}[h]
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\centering
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\caption{Overview of metrics}
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\begin{tabular}{r c c c c c c c}
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% \bottomrule
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% \textbf{Statistic} &\textbf{Mean} &\textbf{St.} & \textbf{Min} &\textbf{Median} &\textbf{Max} \\
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\bottomrule
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\textbf{Statistic} &\textbf{Mean} &\textbf{St.} & \textbf{Min} &\textbf{Median} &\textbf{Max} \\
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% \midrule
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% % \textbf{Contributor level metrics}\hspace{1em} \\
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% Core team &0.38 & 0.49 &0 &0 &1 \\
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% Submission\_exp &107.95 & 233.42 &0 &12 &2284 \\
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% Discussion\_exp &1092.07 & 2939.49 &0 &45 &40978 \\
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% \midrule
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% % \textbf{Pull-request level metrics}\hspace{1em} \\
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% First\_pr &0.25 & 0.44 &0 &0 &1 \\
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% Early\_arrival &0.50 & 0.50 &0 &0.50 &1 \\
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% Churn &313.1 & 1998.92 &0 &14 &45955 \\
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% \#Files &9.79 & 104.61 &0 &1 &3874 \\
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\midrule
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% \textbf{Contributor level metrics}\hspace{1em} \\
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Core team &0.38 & 0.49 &0 &0 &1 \\
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Submission\_exp &107.95 & 233.42 &0 &12 &2284 \\
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Discussion\_exp &1092.07 & 2939.49 &0 &45 &40978 \\
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\midrule
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% \textbf{Pull-request level metrics}\hspace{1em} \\
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First\_pr &0.25 & 0.44 &0 &0 &1 \\
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Early\_arrival &0.50 & 0.50 &0 &0.50 &1 \\
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Churn &313.1 & 1998.92 &0 &14 &45955 \\
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\#Files &9.79 & 104.61 &0 &1 &3874 \\
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% \midrule
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% % \textbf{Review level metrics}\hspace{1em} \\
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% Review\_duration &3610558 & &8 &212672 &124941046 \\
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% \#Comments &6.59 & 14.11 &0 &2 &231 \\
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% \#Comments\_inline &4.427 & 8.39 &0 &2 &165 \\
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\midrule
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% \textbf{Review level metrics}\hspace{1em} \\
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Review\_duration &3610558 & &8 &212672 &124941046 \\
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\#Comments &6.59 & 14.11 &0 &2 &231 \\
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\#Comments\_inline &4.427 & 8.39 &0 &2 &165 \\
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% \toprule
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% \end{tabular}
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% \label{tab:sub_metrics}
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% \end{table*}
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\toprule
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\end{tabular}
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\label{tab:sub_metrics}
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\end{table*}
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% \begin{table*}[h]
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% \centering
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% \caption{Statistical models for \hl{happence} of duplicate pull-requets}
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% \begin{tabular}{r c c }
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% \toprule
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\begin{table*}[h]
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\centering
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\caption{Statistical models for \hl{happence} of duplicate pull-requets}
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\begin{tabular}{r c c }
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\toprule
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% & Estimate & Significance \\
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% \midrule
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% % \textbf{Contributor level metrics}\hspace{1em} \\
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% Core team & & \\
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% First\_pr & & \\
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% \#Prev\_pr & & \\
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& Estimate & Significance \\
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\midrule
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% \textbf{Contributor level metrics}\hspace{1em} \\
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Core team & & \\
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First\_pr & & \\
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\#Prev\_pr & & \\
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% \midrule
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% % \textbf{Pull-request level metrics}\hspace{1em} \\
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% Churn & & \\
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% \#Files & & \\
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% Test\_touched & & \\
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% Sub\_early & & \\
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\midrule
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% \textbf{Pull-request level metrics}\hspace{1em} \\
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Churn & & \\
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\#Files & & \\
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Test\_touched & & \\
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Sub\_early & & \\
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% \midrule
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% % \textbf{Review level metrics}\hspace{1em} \\
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% Review\_duration & & \\
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% \#Comments & & \\
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% \#Comments\_inline & & \\
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% \bottomrule
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% \multicolumn{3}{l}{\emph{*** p \textless 0.001, ** p \textless 0.01, *p \textless 0.05}}
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% \end{tabular}
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% \label{tab:sta_models}
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% \end{table*}
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\midrule
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% \textbf{Review level metrics}\hspace{1em} \\
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Review\_duration & & \\
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\#Comments & & \\
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\#Comments\_inline & & \\
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\bottomrule
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\multicolumn{3}{l}{\emph{*** p \textless 0.001, ** p \textless 0.01, *p \textless 0.05}}
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\end{tabular}
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\label{tab:sta_models}
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\end{table*}
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% ############ 2019-01-12注释掉 ############
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%-( -1
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%-) 1
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(-: 1
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(: 1
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(^ ^) 1
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(^-^) 1
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(^.^) 1
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(^_^) 1
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(o: 1
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(o; 0
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)-: -1
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): -1
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)o: -1
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*) 0
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*\o/* 1
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--^--@ 1
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0:) 1
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38* -1
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8) 1
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8-) 0
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8-0 -1
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8/ -1
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8\ -1
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8c -1
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:# -1
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:'( -1
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:'-( -1
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:( -1
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:) 1
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:*( -1
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:,( -1
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:-& -1
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:-( -1
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:-(o) -1
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:-) 1
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:-* 1
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:-* 1
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:-/ -1
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:-/ 0
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:-D 1
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:-O 0
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:-P 1
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:-S -1
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:-\ -1
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:-\ 0
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:-| -1
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:-} 1
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:/ -1
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:0->-<|: 0
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:3 1
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:9 1
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:D 1
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:E -1
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:F -1
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:O -1
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:P 1
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:P 1
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:S -1
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:X 1
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:[ -1
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:[ -1
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:\ -1
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:] 1
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:_( -1
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:b) 1
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:l 0
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:o( -1
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:o) 1
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:p 1
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:s -1
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:| -1
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:| 0
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:<3A> 1
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:<3A>( -1
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;) 0
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;^) 1
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;o) 0
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</3-1 -1
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<3 1
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<:} 0
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<o< -1
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=( -1
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=) 1
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=[ -1
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=] 1
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>/ -1
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>:( -1
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>:) 1
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>:D 1
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>:L -1
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>:O -1
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>=D 1
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>[ -1
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>\ -1
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>o> -1
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@}->-- 1
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B( -1
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Bc -1
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D: -1
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X( -1
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X( -1
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X-( -1
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XD 1
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XD 1
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XO -1
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XP -1
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XP 1
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^_^ 1
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^o) -1
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x3? 1
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xD 1
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xP -1
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|8C -1
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|8c -1
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|D 1
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}:) 1
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#for review comment @ 2018-04-21
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:bell: 5
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#good:3, great:3, awesome:4
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:+1: 5
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+1 3
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-1 -3
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@ -0,0 +1,24 @@
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##This file contains domain-specific changes to the main sentiment dictionary and evaluative terms.
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##Changes to the main sentiment dictionary are for words that have a different sentiment for the domain
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## or are non-sentiment evaluative words in the domain that are not associated with any particular feature.
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##Domain-specific changes to the main sentiment dictionary take the form
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##Word <tab> sentiment strength (from -5,-4,-3,-2,1,2,3,4,5)
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## e.g., heavy -3
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##Or phrase <tab> sentiment strength (from -5,-4,-3,-2,1,2,3,4,5) for idiomatic phrases
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##Evaluative terms take the form:
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##Object <tab> evaluative term <tab> sentiment strength (from -5,-4,-3,-2,1,2,3,4,5)
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## e.g., screen small -3 (note to mike: very/little)
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##Lines starting with two hashes are ignored
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##Sentiment dictionary additions and changes
|
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##squash 3
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##testing 4
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##Idiomatic phrases
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##doing it 3
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##Evaluative terms
|
||||
lgtm 3
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|
|
@ -306,6 +306,147 @@ def veriry_metrics():
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print "https://github.com/%s/%s/pull/%d"%(u_n, r_n, pr_num)
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# 2019-02-27以后的代码
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# 计算每一个pr所有评论的情感极性
|
||||
# 只看issue级别的；如果有@人的话，说明是在对话，可以先着重看@作者的；应该说越往后的评论越重要
|
||||
|
||||
|
||||
|
||||
|
||||
def get_deci_time(repo_id, dup_pr, mst_pr):
|
||||
'''获取一对重复pr的决策时间 '''
|
||||
# 哪一个被先关闭了或者被先合并了
|
||||
|
||||
# 现获取各自的时间点
|
||||
repo_id = gt_gh[repo_id]
|
||||
dup_ct, dup_dt, dup_D = _rw_dur(repo_id, dup_pr)
|
||||
mst_ct, mst_dt, mst_D = _rw_dur(repo_id, mst_pr)
|
||||
|
||||
# 判断是否有overlap #其实只看dup_ct与mst_dt的关系就可以了，因为dup是晚于mst提交的
|
||||
if dup_ct > mst_dt or mst_ct > dup_dt:
|
||||
return None
|
||||
|
||||
# 判断决策时间
|
||||
if dup_dt < mst_dt:
|
||||
return dup_dt
|
||||
else:
|
||||
return mst_dt
|
||||
|
||||
import re
|
||||
import subprocess
|
||||
import shlex
|
||||
def _txt_clean(text):
|
||||
'''清洗文本'''
|
||||
# 把评论中的一些干扰项去除掉
|
||||
# 代码去掉
|
||||
text = re.sub('(```[\s\S]*?```|``[\s\S]*?``|`[\s\S]*?`)',"@@cmmcode@@",text)
|
||||
# 他人的引用去掉
|
||||
text = re.sub('(>.*\n|on.*\nwrote:)', "@@cmmref@@", text.lower())#去掉引用块
|
||||
# 超链接去掉
|
||||
text = re.sub("((http|ftp|https)://)(([a-zA-Z0-9\._-]+\.[a-zA-Z]{2,6})|([0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}))(:[0-9]{1,4})*(/[a-zA-Z0-9\&%_\./-~-]*)?","@@cmmhttp@@",text)#去掉http
|
||||
|
||||
# 把换行符去掉
|
||||
text = re.sub(r"\n"," ", text)
|
||||
# 把多个空格替换成一个空格
|
||||
text = re.sub(r"\s+"," ", text)
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def _txt_sent(txt):
|
||||
'''一段文本的情感'''
|
||||
# print "\tbc: ", txt.strip()
|
||||
txt = _txt_clean(txt)
|
||||
# print "\tac: ", txt
|
||||
|
||||
ss_data_path = "/Users/lizhixing/Documents/project"
|
||||
cmd_str = "java -jar SentiStrength.jar stdin sentidata SentiStrength_Data/ explain additionalFile ReviewCommentAddition.txt"
|
||||
p = subprocess.Popen(shlex.split(cmd_str), cwd=ss_data_path,
|
||||
stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
|
||||
|
||||
stdout_text, stderr_text = p.communicate(txt)
|
||||
sentis = [item.strip() for item in stdout_text.split("\t")]
|
||||
# extract the positive value, negative value, and explanation.
|
||||
# print "\t", sentis
|
||||
return sentis[:2]
|
||||
|
||||
|
||||
def _cmt_senti(repo_id, pr_num, decide_time):
|
||||
'''在decide_time前提交的几个comment的情感分析'''
|
||||
cursor.execute("select id,author from `pull-request` where prj_id=%s and pr_num=%s",
|
||||
(repo_id, pr_num))
|
||||
pr_id, pr_author = cursor.fetchone()
|
||||
print pr_id, repo_id, pr_num, pr_author
|
||||
|
||||
# 取出pr的所有comment
|
||||
cursor.execute("select author_name, comment_body, comment_type, created_at,id from comments where pr_id=%s",(pr_id,))
|
||||
cmts = cursor.fetchall()
|
||||
print"\ttotal:",len(cmts)
|
||||
|
||||
# 挑选出非作者本人的评论（非决策comment到底还留不留呢？有些是夸奖两句直接接受了）
|
||||
cmts = [item for item in cmts if item[0] != pr_author]
|
||||
print"\tno pr_author:",len(cmts)
|
||||
# print"\n"
|
||||
|
||||
# 按时间排序，从决策时间往前看n个
|
||||
decide_time = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.localtime(decide_time))
|
||||
cmts = [item for item in sorted(cmts, key=lambda x:x[3], reverse=True) if item[3]<=decide_time]
|
||||
if len(cmts) == 0:
|
||||
return
|
||||
senti_num = [0,0] #记录各种极性情感的评论的个数
|
||||
for cmt in cmts:
|
||||
# print "\t", cmt[0], cmt[3], cmt[2]
|
||||
cmt_senti = _txt_sent(cmt[1])
|
||||
if int(cmt_senti[0]) > 1:
|
||||
senti_num[0] += 1
|
||||
if int(cmt_senti[1]) < -1:
|
||||
senti_num[1] += 1
|
||||
# print "\n"
|
||||
|
||||
print "\t SENTI_P: ", senti_num, [1.0*item/len(cmts) for item in senti_num]
|
||||
|
||||
def load_cust_cfg():
|
||||
# 把项目中的定制化配置文件更新到 sentistrength相应目录下
|
||||
ss_dir = ss_data_path = "/Users/lizhixing/Documents/project/SentiStrength_Data"
|
||||
|
||||
with open("%s/ReviewCommentAddition.txt"%(ss_dir,),"w+") as t_fp:
|
||||
t_fp.truncate()
|
||||
with open("custom-config/ReviewCommentAddition.txt","r+") as s_fp:
|
||||
for line in s_fp:
|
||||
t_fp.write(line)
|
||||
with open("%s/EmoticonLookupTable.txt"%(ss_dir,),"w+") as t_fp:
|
||||
t_fp.truncate()
|
||||
with open("custom-config/EmoticonLookupTable.txt","r+") as s_fp:
|
||||
for line in s_fp:
|
||||
t_fp.write(line)
|
||||
# 目前配置的有
|
||||
# lgtm: 3.
|
||||
|
||||
def cmt_sentiment():
|
||||
# 可以这样：每一个评论有这样的极性得分[p,n]，然后看他所有的评论中，p>1的比例有多少，n<-1的比例有多少
|
||||
# 也就说有两个指标，包含积极情感的审阅意见占比； 包含消极情感的审阅意见占比
|
||||
load_cust_cfg()
|
||||
cursor.execute("select prj_id,mst_pr,dup_pr,idn_cmt from duplicate")
|
||||
import random
|
||||
dups = [list(item) for item in cursor.fetchall()] #cvt 2 list to shuffle
|
||||
random.shuffle(dups)
|
||||
for dup in dups[:5]:
|
||||
# 取出pr的相关信息
|
||||
repo_id, dup_pr, mst_pr, idn_cmt = dup
|
||||
print dup
|
||||
|
||||
# 判断两个pr的决策时间
|
||||
decide_time = get_deci_time(repo_id,mst_pr,dup_pr)
|
||||
if decide_time is None:
|
||||
print "no overlapped"
|
||||
print "*"*20
|
||||
continue
|
||||
|
||||
# 挨个判断
|
||||
_cmt_senti(repo_id, dup_pr, decide_time)
|
||||
_cmt_senti(repo_id, mst_pr, decide_time)
|
||||
print "*"*20
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init()
|
||||
# _author_core()
|
||||
|
|
@ -317,4 +458,10 @@ if __name__ == '__main__':
|
|||
|
||||
# metric_dist()
|
||||
|
||||
# veriry_metrics()
|
||||
# veriry_metrics()
|
||||
|
||||
# 测试情感分析效果
|
||||
# load_cust_cfg()
|
||||
# cmt_str = "lgtm, we can merge this pr. +1"
|
||||
# _txt_sent(cmt_str)
|
||||
cmt_sentiment()
|
||||
|
|
|
|||
Loading…
Reference in New Issue