Added data extraction items.

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "editor.wordWrap": "on"
+}

main.typ

@@ -1,5 +1,5 @@
 //#import "@preview/clean-acmart:0.0.1": acmart, acmart-ccs, acmart-keywords, acmart-ref, to-string
-#import "clean-acmart.typ": acmart, acmart-ccs, acmart-keywords, acmart-ref, to-string
+#import "clean-acmart.typ": acmart
 #import "@preview/cetz:0.3.4"
 
 #let title = [Dataflow Analysis for Compiler Optimization]
@@ -16,7 +16,6 @@
   title: title,
   authors: authors,
   copyright: none
-  //page: "A4"
   // Set review to submission ID for the review process or to "none" for the final version.
   // review: [\#001],
 )
@@ -39,7 +38,7 @@
 #set heading(numbering: "1.1.1")
 
 = Abstract
-// define DFA here or in introduction
+// define DFA and CO here or in introduction
 todo
 
 = Introduction
@@ -72,8 +71,11 @@ My search strategy consisted of 4 steps as seen in @sas_fig. \
 ) <sas_search_string> 
 
 The papers from the first steps are collected from the electronic databases ACM Digital Library, IEEE Xplore, Springer Link, Web of Science with the search string seen in @sas_search_string.
-The search string in @sas_search_string was created using the research questions in @research_questions_s and was always applied to the full text of the papers.
-
+The search string in @sas_search_string was created using the research questions in @research_questions_s and was always applied to the full text of the papers. \
+In the second step all duplicates which where returned from multiple databases where removed from the results. \
+In the third step the selection was filtered by applying all selection criteria from @selection_criteria_s. \
+In the forth step I snowballed the previously acquired results. This was to find relevant papers which where not included because of either the search string or the search criteria. \
+Afterwards all papers where evaluated based on the data extraction items mentioned in @data_extraction_s.
 #place(
   bottom + center,
   scope: "parent",
@@ -121,6 +123,45 @@ The search string in @sas_search_string was created using the research questions
   ]
 )
 
+== Selection criteria <selection_criteria_s>
+#[
+  #set enum(numbering: (.., i) => "IC" + str(i))
+]
+#[
+  #set enum(numbering: (.., i) => "EC" + str(i))
+]
+
+== Data extraction <data_extraction_s>
+#place(
+  bottom + center,
+  scope: "parent",
+  float: true,
+  [
+    #set par(leading: 0.3em)
+    #set text(size: 9pt)
+    #figure(
+      caption: [Data items],
+      supplement: "Table",
+      table(
+        columns: (1fr, 8fr, 2fr),
+        stroke: (x, y) => if y == 0 { (bottom: 0.7pt + black) },
+        align: left,
+        inset: (x: 6pt, y: 2pt),
+        [ID], [Data], [Purpose],
+        ..(
+          ([Author(s)], [Documentation]),
+          ([Publication year], [Documentation]),
+          ([Title], [Documentation]),
+          ([Named advantage(s) of DFA for CO], [RQ1]),
+          ([Named disadvantage(s) of DFA for CO], [RQ1]),
+          ([Analyzed compilers], [RQ2]),
+          ([In what way is DFA used], [RQ2])
+        ).enumerate(start: 1).map(((i, arr)) => ([D#i], ..arr)).flatten()
+      )
+    ) <data_extraction_table>
+  ]
+)
+
 #bibliography("refs.bib", title: "References", style: "association-for-computing-machinery")
 
 #colbreak(weak: true)