Added selection criteria and review process overview
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This commit is contained in:
Matthias Veigel
parent
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commit
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GPG Key ID:
2437494E09F13876
68
main.typ
68
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@ -45,6 +45,19 @@ todo
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todo
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todo
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= Methodology
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= Methodology
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This publication was created following the process described in @process_fig. The protocol for the review is divided up into the object of the research see @research_questions_s, the search strategy see @sas_s, the selection criteria see @selection_criteria_s and the data extraction strategy see @data_extraction_s.
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#place(
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bottom + center,
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scope: "parent",
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float: true,
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[
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#figure(
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caption: [Overview of the review process],
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image("review_process.svg")
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) <process_fig>
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]
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)
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== Objective and research questions <research_questions_s>
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== Objective and research questions <research_questions_s>
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The goal of this research paper is to find claims about the advantages and disadvantages of using dataflow analysis for compiler optimization and where DFA is already implemented in Compilers.
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The goal of this research paper is to find claims about the advantages and disadvantages of using dataflow analysis for compiler optimization and where DFA is already implemented in Compilers.
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This goal has been defined in two research questions:
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This goal has been defined in two research questions:
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@ -54,9 +67,8 @@ This goal has been defined in two research questions:
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- RQ2 --- How is dataflow analysis used in current compilers? \
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- RQ2 --- How is dataflow analysis used in current compilers? \
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This questions aims to identify how DFA is already used in current compilers and if it is used during normal compilation or if it has to be explicitly enabled.
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This questions aims to identify how DFA is already used in current compilers and if it is used during normal compilation or if it has to be explicitly enabled.
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== Search and selection strategy
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== Search and selection strategy <sas_s>
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My search strategy consisted of 4 steps as seen in @sas_fig. \
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My search strategy consisted of 4 steps as seen in @sas_fig. \
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#figure(
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#figure(
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caption: [Search string used in electronic databases],
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caption: [Search string used in electronic databases],
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kind: "raw",
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kind: "raw",
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@ -69,7 +81,6 @@ My search strategy consisted of 4 steps as seen in @sas_fig. \
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```
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```
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]
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]
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) <sas_search_string>
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) <sas_search_string>
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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.
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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.
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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. \
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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. \
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In the second step all duplicates which where returned from multiple databases where removed from the results. \
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In the second step all duplicates which where returned from multiple databases where removed from the results. \
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@ -77,17 +88,19 @@ In the third step the selection was filtered by applying all selection criteria
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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. \
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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. \
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Afterwards all papers where evaluated based on the data extraction items mentioned in @data_extraction_s.
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Afterwards all papers where evaluated based on the data extraction items mentioned in @data_extraction_s.
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#place(
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#place(
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bottom + center,
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auto,
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scope: "parent",
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scope: "parent",
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float: true,
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float: true,
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[
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[
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#set par(leading: 0.3em)
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#set par(leading: 0.3em)
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#set text(size: 9pt)
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#set text(size: 8pt)
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#figure(
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#figure(
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caption: [Search and selection process],
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caption: [Search and selection process],
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cetz.canvas({
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cetz.canvas({
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import cetz.draw: *
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import cetz.draw: *
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let bs = (3.3, 1.1)
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let bs = (2.8, 1)
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set-style(stroke: (thickness: 0.5pt))
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rect((0, 0), (rel: bs), name: "acm")
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rect((0, 0), (rel: bs), name: "acm")
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rect((0, -(bs.at(1)+0.3)*1), (rel: bs), name: "ieee")
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rect((0, -(bs.at(1)+0.3)*1), (rel: bs), name: "ieee")
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@ -104,7 +117,7 @@ Afterwards all papers where evaluated based on the data extraction items mention
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line("websci.east", (rel: (0.75, 0)), name: "dld")
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line("websci.east", (rel: (0.75, 0)), name: "dld")
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line("dlu.end", "dld.end", name: "dl")
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line("dlu.end", "dld.end", name: "dl")
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set-style(mark: (end: ">"))
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set-style(mark: (end: "straight"))
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line("dl.50%", "dup.west")
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line("dl.50%", "dup.west")
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line("dup.east", "sel.west")
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line("dup.east", "sel.west")
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line("sel.east", "snow.west")
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line("sel.east", "snow.west")
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@ -117,23 +130,47 @@ Afterwards all papers where evaluated based on the data extraction items mention
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content("dup", align(center)[Duplicate removal \ n = ])
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content("dup", align(center)[Duplicate removal \ n = ])
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content("sel", align(center)[Application of \ selection criteria \ n = ])
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content("sel", align(center)[Application of \ selection criteria \ n = ])
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content("snow", align(center)[Snowballing \ n = ])
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content("snow", align(center)[Snowballing \ n = ])
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content("inc", align(center)[Papers included \ n = ])
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content("inc", align(center)[Publications included \ n = ])
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})
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})
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) <sas_fig>
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) <sas_fig>
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]
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]
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)
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)
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== Selection criteria <selection_criteria_s>
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== Selection criteria <selection_criteria_s>
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For a publication to be relevant it has to satisfy at least one inclusion criteria and not any exclusion criteria. The criteria were chosen to include as any publications as possible but still filter out irrelevant ones.
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#[
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#[
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#v(10pt)
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#set enum(numbering: (.., i) => "IC" + str(i))
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#set enum(numbering: (.., i) => "IC" + str(i))
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+ Publications comparing DFA to other optimization techniques
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+ Publications focusing on a specific compiler (e.g., LLVM, Java JIT, C\# JIT)
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+ Publications providing an implementation for a DFA optimization
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#v(10pt)
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]
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]
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_IC1_ was chosen to help answer _RQ1_. \
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_IC2_ is to include publications which talk about a compiler and how DFA is implemented in it. \
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_IC3_ is to further include publications which directly provide an implementation
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#[
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#[
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#v(10pt)
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#set enum(numbering: (.., i) => "EC" + str(i))
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#set enum(numbering: (.., i) => "EC" + str(i))
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+ Publications which discuss DFA in a non-compiler context
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+ Publications written in a language other than english
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+ Secondary and tertiary publications (e.g., systematic literaturer reviews, surveys)
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+ Publications in the form of tutorial papers, short papers, poster papers, editorials
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+ Publications for which the full text is not available
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#v(10pt)
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]
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]
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_EC1_ is to exclude publications which talk about DFA in other contexts which are not relevant to compiler optimization. \
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_EC2-EC5_ are to exclude publications which don't provide enough information to include them in this publication.
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== Data extraction <data_extraction_s>
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== Data extraction <data_extraction_s>
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Based on the research questions I wrote 9 data items to exrtract from all included publications. @data_extraction_table lists all data items. \
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Data items _D1-D3_ are to document the source of the publication. \
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_D4_ and _D5_ are to explicitly list the advantages and disadvantages for answering _RQ1_. \
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_D6_ and _D7_ show in which compiler DFA was implemented and if it is running directly on a programming language like C++ or if it runs on a intermediate language like LLVM IR. \
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_D8_ lists which optimizations where performed based on the results of DFA and _D9_ lists the limitations of the executed DFA. (e.g., only run on function scope). \
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All data items were extracted from the full text of all included publications.
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#place(
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#place(
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bottom + center,
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auto,
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scope: "parent",
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scope: "parent",
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float: true,
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float: true,
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[
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[
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@ -154,19 +191,26 @@ Afterwards all papers where evaluated based on the data extraction items mention
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([Title], [Documentation]),
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([Title], [Documentation]),
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([Named advantage(s) of DFA for CO], [RQ1]),
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([Named advantage(s) of DFA for CO], [RQ1]),
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([Named disadvantage(s) of DFA for CO], [RQ1]),
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([Named disadvantage(s) of DFA for CO], [RQ1]),
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([Analyzed compilers], [RQ2]),
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([Analyzed compiler(s)], [RQ2]),
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([In what way is DFA used], [RQ2])
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([Targeted language(s) of the optimization], [RQ2]),
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([What optimizations are implemented with DFA], [RQ2]),
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([Limitations of the analysis], [RQ2])
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).enumerate(start: 1).map(((i, arr)) => ([D#i], ..arr)).flatten()
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).enumerate(start: 1).map(((i, arr)) => ([D#i], ..arr)).flatten()
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)
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)
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) <data_extraction_table>
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) <data_extraction_table>
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]
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]
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)
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)
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#bibliography("refs.bib", title: "References", style: "association-for-computing-machinery")
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#colbreak()
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#set heading(numbering: none)
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= References
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#bibliography("refs.bib", title: none, style: "association-for-computing-machinery")
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/*
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#colbreak(weak: true)
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#colbreak(weak: true)
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#set heading(numbering: "A.a.a")
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#set heading(numbering: "A.a.a")
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= Artifact Appendix
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= Artifact Appendix
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In this section we show how to reproduce our findings.
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In this section we show how to reproduce our findings.
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*/
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231
review_process.drawio
Normal file
231
review_process.drawio
Normal file
@ -0,0 +1,231 @@
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3
review_process.svg
Normal file
3
review_process.svg
Normal file
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 40 KiB |
Loading…
x
Reference in New Issue
Block a user