minor

PRCCE/1_introduction.tex

@@ -69,7 +69,7 @@ They reported that reviewers discuss on both
 the appropriateness of the contribution proposal and the correctness of the implemented solution. 
 They only analyzed comments of highly discussed pull-requests which have extended discussions. 
 According to the statistics in their dataset, however, 
-each pull-request gets 2.6 comments in average and only 15.9\% of the pull-request have extended discussions. 
+each pull-request gets 2.6 comments in average and only about 16\% of the pull-request have extended discussions. 
 The small proportion of explored pull-requests may result in bias to the experiment results
 and constrain the generalization of the their findings. 
 Therefore, we tried to revisit the review topics in pull-based development model 

PRCCE/3_approach.tex

@@ -29,7 +29,7 @@ our research approach consists of the following steps.
 
 	\item \textit{Data collection}:
 	 	In our prior work, 
-	 	we collected 4896 projects that have the most number of pull-requests on Github.  
+	 	we collected 4,896 projects that have the most number of pull-requests on Github.  
 	 	This dataset is crawled through the official API offered by GitHub 
 	 	and updated in the current study. 
 	 	In addition, we also use the data released by 
@@ -91,10 +91,10 @@ The dataset is composed of two sources:
 {\color{red}GHTorrent MySQL dump} released in Jun. 2016 
 and our own crawled data from GitHub. 
 From GHTorrent, we can get a list of projects together with their basic information 
-such as programming language, hosting time, the number of forks, the list of pull-requests. 
+such as programming language, hosting time, the number of forks, and the list of pull-requests. 
 {\color{red}
 For our own dataset, we have crawled the text content of 
-pull-requests (i.e., title, description) and review comments 
+pull-requests (\ie title, description) and review comments 
 according to the urls provided by GHTorrent.
 }
 Finally, the two sources are linked by the id of projects and pull-request number. 
@@ -124,12 +124,12 @@ In total, our dataset contains 27,339 pull-requests and 147,367 review comments.
 Previous work has studied the challenges faced by pull-request reviewers 
 and the issues introduced by pull-request submitters~\cite{Gousios:2014b,Tsay:2014a}. 
 Inspired by their work, we decide to comprehensively observe 
-the motivations of reviewers in joining the code review in depth
+the topics of code reviews in depth
 rather than merely focusing on technical and nontechnical perspectives.
 
 % !!!!!!!!!! card sort 
 We conducted a card sort~\cite{Bacchelli:2013} 
-to determine the taxonomy scheme, 
+to determine the taxonomy schema, 
 which is executed manually through an iterative process 
 of reading and analyzing review comments randomly collected from the three projects. 
 The following steps are to executed to define the taxonomy.