Reduction in sections

1 files changed, 5 insertions, 3 deletions

diff --git a/intro.tex b/intro.tex
index 4d3f311..11fc016 100644
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+++ b/intro.tex
@@ -30,7 +30,7 @@ In this paper, we bring fuzzing to the HLS context.
 
 \begin{example}[A miscompilation bug in Vivado HLS]
 \label{ex:vivado_miscomp}
-Figure~\ref{fig:vivado_bug1} shows a program that produces the wrong result during RTL simulation in Xilinx Vivado HLS v2018.3, v2019.1 and v2019.2.\footnote{This program, like all the others in this paper, includes a \code{main} function, which means that it compiles straightforwardly with GCC. To compile it with an HLS tool, we rename \code{main} to \code{result}, synthesise that function, and then add a new \code{main} function as a testbench that calls \code{result}.} The bug was initially revealed by a randomly generated program of around 113 lines, which we were able to reduce to the minimal example shown in the figure.  This bug was also reported to Xilinx and confirmed to be a bug.\footnote{https://bit.ly/3mzfzgA}
+Figure~\ref{fig:vivado_bug1} shows a program that produces the wrong result during RTL simulation in Xilinx Vivado HLS v2018.3, v2019.1 and v2019.2.\footnote{This program, like all the others in this paper, includes a \code{main} function, which means that it compiles straightforwardly with GCC. To compile it with an HLS tool, we rename \code{main} to \code{result}, synthesise that function, and then add a new \code{main} function as a testbench that calls \code{result}.} The bug was initially revealed by a randomly generated program of around 113 lines, which we were able to reduce to the minimal example shown in the figure.  This bug was also reported to Xilinx and confirmed to be a bug.\footnote{Link to Xilinx bug report redacted for review.}% \footnote{https://bit.ly/3mzfzgA}
 The program repeatedly shifts a large integer value \code{x} right by the values stored in array \code{arr}.
 Vivado HLS returns \code{0x006535FF}, but the result returned by GCC (and subsequently confirmed manually to be the correct one) is \code{0x046535FF}.
 \end{example}
@@ -52,14 +52,16 @@ int main() {
 
 The example above demonstrates the effectiveness of fuzzing. It seems unlikely that a human-written test-suite would discover this particular bug, given that it requires several components all to coincide -- a for-loop, shift-values accessed from an array with at least six elements, and a rather random-looking value for \code{x} -- before the bug is revealed! 
 
-Yet this example also begs the question: do bugs found by fuzzers really \emph{matter}, given that they are usually found by combining language features in ways that are vanishingly unlikely to happen `in the real world'~\cite{marcozzi+19}. This question is especially pertinent for our particular context of HLS tools, which are well-known to have restrictions on the language features that they handle. Nevertheless, we would argue that although the \emph{test-cases} we generated do not resemble the programs that humans write, the \emph{bugs} that we exposed using those test-cases are real, and \emph{could also be exposed by realistic programs}. Moreover, it is worth noting that HLS tools are not exclusively provided with human-written programs to compile: they are often fed programs that have been automatically generated by another compiler. Ultimately, we believe that any errors in an HLS tool are worth identifying because they have the potential to cause problems, either now or in the future. And problems caused by HLS tools going wrong (or indeed any sort of compiler for that matter) are particularly egregious, because it is so difficult for end-users to identify whether the fault lies with the tool or with the program it has been given to compile. 
+Yet this example also begs the question: do bugs found by fuzzers really \emph{matter}, given that they are usually found by combining language features in ways that are vanishingly unlikely to happen `in the real world'~\cite{marcozzi+19}. This question is especially pertinent for our particular context of HLS tools, which are well-known to have restrictions on the language features that they handle. Nevertheless, although the \emph{test-cases} we generated do not resemble the programs that humans write, the \emph{bugs} that we exposed using those test-cases are real, and \emph{could also be exposed by realistic programs}.
+%Moreover, it is worth noting that HLS tools are not exclusively provided with human-written programs to compile: they are often fed programs that have been automatically generated by another compiler.
+Ultimately, we believe that any errors in an HLS tool are worth identifying because they have the potential to cause problems, either now or in the future. And problems caused by HLS tools going wrong (or indeed any sort of compiler for that matter) are particularly egregious, because it is so difficult for end-users to identify whether the fault lies with their design or the HLS tool.
 
 \subsection{Our approach and results}
 
 Our approach to fuzzing HLS tools comprises three steps. 
 First, we use Csmith~\cite{yang11_findin_under_bugs_c_compil} to generate thousands of valid C programs from within the subset of the C language that is supported by all the HLS tools we test. We also augment each program with a random selection of HLS-specific directives. Second, we give these programs to four widely used HLS tools: Xilinx Vivado HLS~\cite{xilinx20_vivad_high_synth}, LegUp HLS~\cite{canis13_legup}, the Intel HLS Compiler, which is also known as i++~\cite{intel20_sdk_openc_applic} and finally Bambu~\cite{pilato13_bambu}. Third, if we find a program that causes an HLS tool to crash, or to generate hardware that produces a different result from GCC, we reduce it to a minimal example with the help of the \creduce{} tool~\cite{creduce}.
 
-Our testing campaign revealed that all three tools could be made to crash while compiling or to generate wrong RTL.  In total, \totaltestcases{} test cases were run through each tool out of which \totaltestcasefailures{} test cases failed in at least one of the tools.  Test case reduction was then performed on some of these failing test cases to obtain at least \numuniquebugs{} unique failing test cases.
+Our testing campaign revealed that all four tools could be made to generate an incorrect design.  In total, \totaltestcases{} test cases were run through each tool out of which \totaltestcasefailures{} test cases failed in at least one of the tools.  Test case reduction was then performed on some of these failing test cases to obtain at least \numuniquebugs{} unique failing test cases.
     
 To investigate whether HLS tools are getting more or less reliable over time, we also tested three different versions of Vivado HLS (v2018.3, v2019.1, and v2019.2).  We found far fewer failures in versions v2019.1 and v2019.2 compared to v2018.3, but we also identified a few test-cases that only failed in versions v2019.1 and v2019.2, which suggests that some new features may have introduced bugs.