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\begin{document}

\title{SqC: A Tree-Sitter Based Static Analyzer for\\CERT C Coding Standard Compliance}

\author{Brandon Arrendondo\\
{\tt\small brandon.arrendondo@bissell.com}
\and
Eric Buehler\\
{\tt\small eric.buehler@bissell.com}
\and
Tristan Vanfossen\\
{\tt\small tristan.vanfossen@bissell.com}
}
\maketitle

\begin{abstract}
Static analysis tools for C either cover narrow subsets of known
vulnerability classes or require heavyweight build infrastructure that
limits adoption.  This paper presents SqC, a static analyzer that
checks C source code for compliance with 290 rules from the SEI CERT C
Coding Standard---significantly broader coverage than any single
open-source alternative.  SqC uses tree-sitter for incremental parsing,
constructs per-function control-flow graphs, performs forward dataflow
analysis for null pointer state tracking, and employs inter-procedural
function summaries to reduce false positives across translation units.

We evaluate SqC against the NIST Juliet Test Suite v1.3 (50,038 test
files across 74 CWE categories) and 5 real-world open-source C codebases
totaling 1.27 million lines of code.  Through iterative
refinement---including cross-file function summaries, value-range analysis,
and CFG-based dead-branch pruning---the true positive rate improved from
41.1\% to 67.5\%, with 34 of 74 CWEs achieving 100\% precision (zero
false positives).  On a five-tool comparison against cppcheck, clang-tidy,
Infer, and Frama-C, SqC covers 5--12$\times$ more CWE categories while
exceeding Frama-C's per-CWE precision on overlapping CWEs.  On real-world code, SqC detects
vulnerability classes---including POSIX misuse, integer overflow, and
inter-procedural null dereference---that cppcheck and clang-tidy do not
cover.
\end{abstract}

%% ============================================================
\section{Introduction}
\label{sec:intro}

The C programming language remains dominant in systems software,
embedded systems, and security-critical infrastructure.  Despite decades
of research in programming language safety, C's lack of memory safety
guarantees continues to produce vulnerabilities: buffer overflows, null
pointer dereferences, integer overflows, and use-after-free defects
account for the majority of CVEs in C codebases~\cite{cwe-top25}.

The SEI CERT C Coding Standard~\cite{cert-c} codifies 120+ rules and
200+ recommendations spanning 17 categories, from integer handling to
concurrency to POSIX-specific interfaces.  While the standard is widely
referenced in secure coding guidelines (e.g., MISRA C, IEC 62443),
tool support for comprehensive CERT C checking remains fragmented.
Commercial tools such as Coverity~\cite{coverity}, Klocwork, and
Polyspace implement subsets of CERT C rules alongside proprietary
checkers, but their closed-source nature limits reproducibility.
Open-source alternatives like cppcheck~\cite{cppcheck} and
clang-tidy~\cite{clang-tidy} implement approximately 20 CERT C checks
each, leaving the majority of the standard uncovered.

This paper presents SqC (\textbf{S}oftware Code \textbf{Q}uality for
\textbf{C}), a static analysis tool that implements 290 CERT C rules
across all 17 categories.  The paper describes the tool's architecture,
the iterative process used to reduce false positives, and the results of
evaluating SqC against both a standardized benchmark and real-world
codebases.

The rest of this paper is organized as follows.  \Cref{sec:background}
covers the CERT C standard and the Juliet benchmark.
\Cref{sec:architecture} describes SqC's architecture.
\Cref{sec:methodology} details the testing methodology.
\Cref{sec:results} presents benchmark results.
\Cref{sec:fp-reduction} walks through the iterative false positive
reduction process.  \Cref{sec:worked-example} provides a worked
example.  \Cref{sec:limitations} discusses limitations.
\Cref{sec:related} covers related work.  \Cref{sec:conclusion}
concludes.

%% ============================================================
\section{Background}
\label{sec:background}

\subsection{SEI CERT C Coding Standard}

The SEI CERT C Coding Standard is maintained by the Software
Engineering Institute at Carnegie Mellon University.  It defines rules
and recommendations organized into 17 categories, shown in
\cref{tab:cert-categories}.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}llr@{}}
\toprule
\textbf{Prefix} & \textbf{Category} & \textbf{Rules} \\
\midrule
API & API Usage & 9 \\
ARR & Arrays & 9 \\
CON & Concurrency & 23 \\
DCL & Declarations & 31 \\
ENV & Environment & 8 \\
ERR & Error Handling & 11 \\
EXP & Expressions & 31 \\
FIO & File I/O & 35 \\
FLP & Floating Point & 13 \\
INT & Integers & 23 \\
MEM & Memory Mgmt & 17 \\
MSC & Miscellaneous & 13 \\
POS & POSIX & 21 \\
PRE & Preprocessor & 16 \\
SIG & Signals & 7 \\
STR & Strings & 16 \\
WIN & Windows & 7 \\
\midrule
& \textbf{Total} & \textbf{290}\textsuperscript{$\dagger$} \\
\bottomrule
\end{tabular}
\caption{CERT C rule categories implemented in SqC.
\textsuperscript{$\dagger$}Includes both mandatory rules and advisory
recommendations.}
\label{tab:cert-categories}
\end{table}

Each rule addresses a specific class of undefined behavior, security
vulnerability, or quality defect.  For example, EXP34-C (``Do not
dereference null pointers'') maps directly to CWE-476; INT32-C
(``Ensure that operations on signed integers do not result in
overflow'') maps to CWE-190.  The standard provides a rule-to-CWE
mapping that enables systematic benchmarking.

\subsection{NIST Juliet Test Suite}

The Juliet Test Suite v1.3~\cite{juliet} (commit \texttt{f88433e})
is developed by NIST's
Software Assurance Metrics and Tool Evaluation (SAMATE) project.
It contains 64,099 C/C++ test cases across 118 CWE categories,
organized into ``good'' functions (correct code that should not
trigger warnings) and ``bad'' functions (code containing the
specified vulnerability).

Juliet provides a controlled evaluation environment: each test case
isolates a single vulnerability type with known ground truth, enabling
precise measurement of true positive (TP) and false positive (FP) rates
per CWE.

Juliet also has known limitations worth acknowledging.  Test cases use
synthetic patterns that may not reflect real-world coding idioms.  The
structured naming conventions (functions prefixed with \texttt{bad} or
\texttt{good}) can inadvertently advantage or disadvantage
pattern-matching tools.  And Juliet uses explicit C types
(\texttt{int}, \texttt{unsigned int}) rather than \texttt{stdint.h}
typedefs (\texttt{uint32\_t}), so fixes targeting typedef resolution
show impact on real-world code but not on Juliet.

These limitations are why we supplement Juliet with real-world
evaluation.

\subsection{Real-World Benchmarks}

Five open-source C projects serve as real-world benchmarks:
libcrc (2K LOC), mosquitto (89K LOC), curl (240K LOC),
sqlite (402K LOC), and hostap (541K LOC).  These span embedded
libraries, network servers, databases, and large-scale infrastructure.

%% ============================================================
\section{Architecture}
\label{sec:architecture}

SqC is implemented in Rust (2021 edition) and uses tree-sitter for
parsing.  The analysis pipeline proceeds in four stages, illustrated
in \cref{fig:pipeline}.

\begin{figure}[t]
\centering
\begin{tikzpicture}[
    node distance=0.6cm,
    box/.style={rectangle, draw, fill=blue!8, text width=5.8cm,
                minimum height=0.7cm, align=center, font=\footnotesize},
    arrow/.style={->, thick}
]
\node[box] (parse) {1. Tree-sitter Parse $\rightarrow$ Concrete Syntax Tree};
\node[box, below=of parse] (prescan) {2. Cross-file Prescan $\rightarrow$ Function Summaries};
\node[box, below=of prescan] (cfg) {3. Per-function CFG + Dataflow Analysis};
\node[box, below=of cfg] (rules) {4. Rule Evaluation (290 CERT C checkers)};
\draw[arrow] (parse) -- (prescan);
\draw[arrow] (prescan) -- (cfg);
\draw[arrow] (cfg) -- (rules);
\end{tikzpicture}
\caption{SqC analysis pipeline.  Each stage enriches the context
available to subsequent stages.}
\label{fig:pipeline}
\end{figure}

\subsection{Stage 1: Parsing with Tree-sitter}

SqC uses tree-sitter~\cite{tree-sitter} to produce a concrete syntax
tree (CST) for each C source file.  Unlike tools built on Clang's AST
(which requires a full compilation database), tree-sitter operates on
individual files without preprocessing.  This trades semantic precision
for practical deployability: SqC can analyze any C file without build
system integration.

The primary limitation is the inability to resolve preprocessor macros
and \texttt{\#include} directives.  SqC mitigates this through a
built-in database of approximately 370 standard library and platform
API functions (\cref{sec:stdlib-db}).

\subsection{Stage 2: Cross-File Prescan}
\label{sec:prescan}

When invoked with the \texttt{-d} (directory) flag, SqC performs a
pre-scan pass over all C files in the specified directories, collecting:

\begin{itemize}
    \item \textbf{Function definitions}: name, file, parameter count,
        and return type.
    \item \textbf{Function summaries}: per-parameter null state
        (DefinitelyNull, PossiblyNull, NotNull, Unknown) derived from
        call-site argument analysis across the project.
    \item \textbf{Local variable states}: within each function body,
        assignments to local variables are tracked to resolve argument
        values passed at call sites.
\end{itemize}

This was the single largest contributor to false positive reduction,
removing 148,622 FPs (31.2\%) in one iteration (\cref{sec:fp-cross-file}).

\subsection{Stage 3: Control-Flow Graphs and Dataflow}

For each function, SqC constructs a control-flow graph (CFG) handling
\texttt{if}/\texttt{else}, \texttt{switch}/\texttt{case}, loops with
\texttt{break}/\texttt{continue}, early returns, and \texttt{goto}
statements.

On the CFG, SqC performs forward dataflow analysis for null pointer
state tracking.  The null state lattice is:

\begin{equation}
\text{NullState} \in \{\text{Unknown}, \text{DefinitelyNull},
    \text{PossiblyNull}, \text{NotNull}\}
\end{equation}

Edge refinement propagates null information through branch conditions:
after \texttt{if (p != NULL)}, the true branch marks \texttt{p} as
NotNull while the false branch marks it as PossiblyNull.

The CFG infrastructure also supports:
\begin{itemize}
    \item A \textbf{constant evaluator} that resolves \texttt{sizeof}
        expressions, C standard limit macros (\texttt{INT\_MAX},
        \texttt{UINT64\_MAX}), file-scope \texttt{static const int}
        declarations, and compile-time arithmetic.
    \item \textbf{Dead-branch pruning}: when a branch condition
        resolves to a compile-time constant (literal, \texttt{\#define},
        or \texttt{static const}), only the feasible branch is included
        in the CFG, improving precision for all downstream analyses.
    \item \textbf{Initialization state tracking} for detecting
        use of uninitialized variables through conditional paths.
    \item \textbf{Value-range analysis} (VRA) for integer and
        floating-point overflow rules, tracking variable ranges through
        assignments, branches, and loop bounds.
\end{itemize}

\subsection{Stage 4: Rule Evaluation}

Each of the 290 rules is implemented as a Rust struct implementing
the \texttt{CertRule} trait.  Rules range from simple AST pattern
matchers (e.g., MSC24-C: obsolete function detection) to complex
multi-pass analyzers (e.g., EXP34-C: null dereference detection
using CFG-based dataflow with inter-procedural call-site propagation).

\textbf{UTF-8 source encoding.}  Tree-sitter represents all source
positions as byte offsets into the raw UTF-8 source file.  Industrial
C codebases routinely embed non-ASCII content in comments and string
literals: comments written in Chinese, Japanese, Arabic, Cyrillic, or
other scripts are common in embedded and international software
projects.  Rule implementations that inspect raw source text must
therefore use byte-indexed string operations throughout.
Character-counted indices produce incorrect slices---or runtime panics
in languages with strict Unicode safety---when a multi-byte sequence
falls before a slice boundary.  For example, a Chinese full-width
parenthesis (\texttt{U+FF08}, three UTF-8 bytes) encountered during
real-world analysis triggered a panic in the ARR00-C and PRE02-C
checkers, both of which were using \texttt{chars().enumerate()} indices
(character positions) as byte offsets.  SqC enforces the requirement
for byte-safe slicing through \texttt{char\_indices()}-based iteration
and includes UTF-8 regression test cases in each affected rule's test
suite.

\subsection{Standard Function Database}
\label{sec:stdlib-db}

Since tree-sitter cannot follow \texttt{\#include} directives, SqC
maintains a built-in database of approximately 370 known functions:
$\sim$180 C11 standard library, $\sim$90 POSIX, and $\sim$100 Windows
API functions.  This database serves multiple purposes: DCL31-C/DCL07-C
use it to avoid flagging standard library calls as undeclared; ERR33-C
uses it to identify functions whose return values must be checked;
EXP10-C uses a pure-function whitelist to exclude side-effect-free
calls from unsequenced evaluation warnings.

%% ============================================================
\section{Testing Methodology}
\label{sec:methodology}

\subsection{Juliet Benchmark Protocol}

The Juliet benchmark operates in two modes:

\textbf{Full mode}: All 290 rules are applied to all 118 CWEs.  This
measures overall tool behavior but introduces noise from rules
triggering on CWEs they were not designed to detect.

\textbf{Fast mode}: Per-CWE rule manifests restrict each scan to only
the CERT C rules mapped to that CWE.  A mapping script generates 147
TOML manifest files from the official CERT C rule-to-CWE mapping.
This eliminates cross-CWE noise and reduces scan time by approximately
10$\times$.  All results from v0.3.20 onward use fast mode.

For each CWE directory, the benchmark invokes SqC with cross-file
analysis enabled, then classifies each violation by file name:
violations in \texttt{*bad*} files are True Positives; violations in
\texttt{*good*} files are False Positives.  Results are stored in a
SQLite database with per-violation granularity.

\textbf{TP rate} is defined as $\frac{\text{TP}}{\text{TP}+\text{FP}}$
(precision), not recall.  A ``bad'' file may contain multiple violation
types, but only the intended CWE is scored as TP.

\subsection{Real-World Benchmark Protocol}

Five open-source C projects (\cref{tab:realworld-codebases}) are
analyzed with SqC, cppcheck (v2.10), and clang-tidy (v21.1.6).

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrrl@{}}
\toprule
\textbf{Project} & \textbf{C Files} & \textbf{LOC} & \textbf{Commit} \\
\midrule
libcrc & 16 & 2,130 & \texttt{7719e21} \\
mosquitto & 384 & 88,717 & \texttt{d3ee5c5} \\
curl & 696 & 240,331 & \texttt{3e198f7} \\
sqlite & 312 & 404,701 & \texttt{b1a73ba} \\
hostap & 504 & 539,617 & \texttt{dcee604} \\
\midrule
\textbf{Total} & \textbf{1,912} & \textbf{1,275,496} & \\
\bottomrule
\end{tabular}
\caption{Real-world benchmark codebases.}
\label{tab:realworld-codebases}
\end{table}

Real-world analysis revealed a robustness requirement absent from the
Juliet benchmark: industrial embedded codebases frequently contain
comments written in non-Latin scripts.  Running SqC against a battery
management system codebase with Chinese-language function comments
uncovered a latent crash in three rule checkers caused by unsafe
UTF-8 string slicing (\cref{sec:architecture}).  The Juliet test suite
contains only ASCII source, so this class of defect is invisible to
benchmark-only validation; it surfaced exclusively through real-world
deployment.

An important caveat for interpreting the cross-tool results: SqC
implements 290 rules while cppcheck and clang-tidy implement
approximately 20 each.  The 100$\times$ difference in raw violation
counts reflects coverage breadth, not false positive rate.

%% ============================================================
\section{Results}
\label{sec:results}

\subsection{Juliet Benchmark Results}

\Cref{tab:juliet-summary} summarizes SqC v0.3.119's performance on
the Juliet Test Suite in fast mode.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lr@{}}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Rules implemented & 290 \\
CWEs scanned (fast mode) & 74 \\
Test files & 50,038 \\
True Positives & 24,345 \\
False Positives & 11,702 \\
TP Rate (precision) & 67.5\% \\
Per-file detection rate & 40.8\% \\
100\% precision CWEs & 34 \\
\bottomrule
\end{tabular}
\caption{SqC v0.3.119 Juliet benchmark summary (fast mode).}
\label{tab:juliet-summary}
\end{table}

\subsubsection{Precision Tiers}

The 74 CWEs tested fall into four precision tiers, shown in
\cref{tab:precision-tiers}.  The distribution is encouraging: over
four-fifths of CWEs achieve either perfect or high precision, while
only two CWEs fall below 33\%.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrrr@{}}
\toprule
\textbf{Tier} & \textbf{CWEs} & \textbf{TP Rate} & \textbf{TP} \\
\midrule
100\% Precision & 34 & 100\% & 6,182 \\
High ($>$50\%) & 26 & 50--99\% & 17,801 \\
Medium (33--50\%) & 6 & 33--50\% & 355 \\
Low ($<$33\%) & 2 & 3--8\% & 7 \\
Zero Detection & 6 & --- & 0 \\
\midrule
\textbf{Total} & \textbf{68}\textsuperscript{$\dagger$} & & \textbf{24,345} \\
\bottomrule
\end{tabular}
\caption{CWE distribution by precision tier (fast mode, v0.3.119).
\textsuperscript{$\dagger$}6 CWEs had no test files.}
\label{tab:precision-tiers}
\end{table}

\Cref{tab:perfect-cwes} lists the 34 CWEs with 100\% precision.  These
represent vulnerability classes where SqC produces zero false
positives---every reported violation is a true defect.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrr@{}}
\toprule
\textbf{CWE} & \textbf{Description} & \textbf{TP} \\
\midrule
CWE-78  & OS Command Injection & 2,810 \\
CWE-690 & Null Deref from Return & 558 \\
CWE-563 & Unused Variable & 289 \\
CWE-761 & Free Pointer Not at Start & 276 \\
CWE-590 & Free Memory Not on Heap & 240 \\
CWE-253 & Incorrect Check of Return Value & 432 \\
CWE-789 & Uncontrolled Mem Alloc & 190 \\
CWE-666 & Wrong Phase of Lifetime & 162 \\
CWE-252 & Unchecked Return Value & 144 \\
CWE-114 & Process Control & 126 \\
CWE-244 & Heap Inspection & 98 \\
CWE-591 & Data in Improperly Locked Mem & 74 \\
CWE-273 & Dropped Privileges & 72 \\
CWE-226 & Sensitive Info Uncleared & 68 \\
\multicolumn{2}{@{}l}{\emph{+ 20 more CWEs}} & 890 \\
\midrule
\textbf{Total} & & \textbf{6,182} \\
\bottomrule
\end{tabular}
\caption{CWEs with 100\% precision (zero false positives).}
\label{tab:perfect-cwes}
\end{table}

\subsubsection{Top Rules by Detection Volume}

\Cref{tab:top-rules} shows the 10 rules contributing the most true
positives.  These rules account for 69\% of all TPs.  The FP rates
vary significantly: EXP34-C achieves 13\% FP rate (CFG-based null-state
dataflow), while STR31-C is at 53\% (buffer size estimation without
alias analysis).  This variation reflects the structural difficulty of
each analysis.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrrrl@{}}
\toprule
\textbf{Rule} & \textbf{TP} & \textbf{FP} & \textbf{FP\%} & \textbf{Primary CWEs} \\
\midrule
INT32-C & 2,826 & 1,863 & 40 & 190, 191, 680 \\
ARR38-C & 2,136 & 684  & 24 & 121, 126, 124 \\
INT31-C & 2,122 & 1,248 & 37 & 195, 194, 197 \\
ARR30-C & 1,814 & 1,121 & 38 & 121, 122, 126 \\
STR31-C & 1,464 & 1,671 & 53 & 121, 122, 124 \\
FIO30-C & 1,200 & 696  & 37 & 134 \\
EXP33-C & 1,110 & 888  & 44 & 457, 758, 665 \\
INT30-C & 952   & 951  & 50 & 190, 191, 680 \\
EXP34-C & 850   & 127  & 13 & 690, 476 \\
MEM31-C & 761   & 220  & 22 & 401 \\
\bottomrule
\end{tabular}
\caption{Top 10 rules by true positive volume (fast mode, v0.3.119).}
\label{tab:top-rules}
\end{table}

\subsubsection{TP Rate Progression}

\Cref{fig:tp-rate-progression} shows the TP rate progression across
the project's development history.  The full-suite baseline started at
41.1\% (v0.2.1); after 12 rounds of FP reduction, it reached 44.6\%
(v0.2.25).  The switch to fast-mode benchmarking (v0.3.20) established
a new baseline of 45.8\%, which has since improved to 67.5\% through
value-range analysis, CFG constant resolution, cross-function taint
tracking, and targeted per-CWE FP reduction.

\begin{figure}[t]
\centering
\begin{tikzpicture}
\begin{axis}[
    width=\columnwidth,
    height=5cm,
    xlabel={Version},
    ylabel={TP Rate (\%)},
    ymin=40, ymax=70,
    xtick=data,
    xticklabels={v0.2.1, v0.2.7, v0.2.23, v0.3.20, v0.3.37, v0.3.53, v0.3.75, v0.3.84, v0.3.99, v0.3.119},
    xticklabel style={rotate=45, anchor=east, font=\footnotesize},
    ylabel style={font=\footnotesize},
    xlabel style={font=\footnotesize},
    tick label style={font=\footnotesize},
    nodes near coords,
    nodes near coords style={font=\tiny},
    every node near coord/.append style={anchor=south},
    grid=major,
    grid style={dashed, gray!30},
]
\addplot[color=blue, mark=*, thick] coordinates {
    (1, 41.1) (2, 44.5) (3, 44.6) (4, 45.8) (5, 48.4) (6, 52.0) (7, 56.3) (8, 60.2) (9, 63.6) (10, 67.5)
};
\end{axis}
\end{tikzpicture}
\caption{TP rate progression from baseline (v0.2.1) through current
(v0.3.119).  The gap between v0.2.23 and v0.3.20 reflects the switch
from full-suite to fast-mode benchmarking.}
\label{fig:tp-rate-progression}
\end{figure}

\subsection{Competitor Comparison}
\label{sec:competitor}

\Cref{tab:competitor} compares SqC against four open-source tools on 15
overlapping Juliet CWEs.  All tools were run on the same test files with
TP/FP classified using identical Juliet ground truth (procedure names
and \texttt{OMITBAD}/\texttt{OMITGOOD} guards).

\begin{table*}[t]
\centering
\footnotesize
\setlength{\tabcolsep}{4pt}
\begin{tabular}{@{}llrrrrr@{}}
\toprule
\textbf{CWE} & \textbf{Description} & \textbf{SqC} & \textbf{cppcheck} & \textbf{clang-tidy} & \textbf{Infer} & \textbf{Frama-C} \\
\midrule
121 & Stack Buffer Overflow       & 57.5 & 40.5 & \textbf{86.6} & 38.8 & --- \\
122 & Heap Buffer Overflow        & 60.8 & 45.0 & \textbf{89.5} & 37.8 & --- \\
124 & Buffer Underwrite           & 58.3 & 40.5 & \textbf{90.4} & 33.9 & --- \\
127 & Buffer Underread            & 82.4 & 42.7 & \textbf{93.2} & 33.9 & --- \\
190 & Integer Overflow            & 60.9 & 27.5 & \textbf{94.3} & --- & 58.1 \\
191 & Integer Underflow           & 55.3 & 27.2 & \textbf{94.4} & --- & 63.1 \\
197 & Numeric Truncation          & 70.2 & 40.0 & 94.5 & --- & \textbf{100.0} \\
369 & Divide by Zero              & 56.0 & 27.4 & \textbf{94.7} & --- & 43.4 \\
401 & Memory Leak                 & 77.6 & 25.7 & \textbf{83.9} & 54.7 & --- \\
415 & Double Free                 & 58.2 & 33.8 & \textbf{80.0} & 63.0 & --- \\
416 & Use After Free              & \textbf{92.9} & 29.6 & 60.3 & 2.0  & --- \\
476 & Null Pointer Deref          & 61.9 & 39.7 & \textbf{94.3} & 66.1 & 64.2 \\
680 & Int Overflow $\to$ BOF      & 55.3 & 40.8 & \textbf{95.9} & --- & 82.6 \\
690 & Null Deref from Return      & \textbf{100.0} & 45.7 & 88.0 & 60.0 & --- \\
761 & Free Not at Start           & \textbf{100.0} & 45.7 & 94.3 & 58.1 & --- \\
\midrule
\multicolumn{2}{@{}l}{\textbf{Overall TP rate}} & 62.5 & 36.4 & \textbf{91.6} & 43.6 & 61.0 \\
\multicolumn{2}{@{}l}{\textbf{CWEs tested}}     & \textbf{74} & 15 & 15 & 10 & 6 \\
\bottomrule
\end{tabular}
\caption{TP rate (\%) comparison on overlapping Juliet CWEs.
\textbf{Bold} = highest per row.  ``---'' = CWE not tested by that tool.
SqC v0.3.119; cppcheck 2.10; clang-tidy (LLVM 21.1); Infer v1.2.0; Frama-C 32.0 EVA.
Competitor numbers are held fixed; SqC's column reflects v0.3.119.
All tools run on same test files (28{,}488 total across 15 CWEs).}
\label{tab:competitor}
\end{table*}

Four patterns emerge.  First, \textbf{clang-tidy achieves the highest
overall precision} (91.6\%) with only 116 false positives across all
15 CWEs.  Its Clang-based static analyzer proves paths rigorously,
sacrificing recall for near-zero FP.  However, it detects fewer total
violations than SqC (13{,}952 vs.\ 24{,}345 TP).

Second, \textbf{SqC has surpassed Frama-C} on the 15 overlapping CWEs:
62.5\% vs.\ Frama-C's 61.0\%, while covering 74 CWEs vs.\ Frama-C's 6.
SqC exceeds Frama-C on CWE-369 (56.0\% vs.\ 43.4\%), CWE-190 (60.9\%
vs.\ 58.1\%), and CWE-191 (55.3\% vs.\ 63.1\% for Frama-C's advantage
here remaining).

Third, \textbf{SqC wins on three CWEs outright}: CWE-690 (100\%, where
inter-procedural null propagation from return values eliminates all FPs);
CWE-761 (100\%, structural pattern matching on free-pointer arithmetic);
and CWE-416 use-after-free (92.9\% vs.\ clang-tidy's 60.3\%), where
MEM01-C's flow-sensitive deref tracking after \texttt{free} distinguishes
the good/bad paths more reliably than clang-tidy's pattern checks.

Fourth, \textbf{cppcheck produces the most findings but lowest precision}
(36.4\%), reflecting its heuristic approach.  Its 29{,}377 TPs represent
high recall but at the cost of 51{,}361 FPs.

The coverage gap remains the key differentiator: SqC tests 74 CWEs
spanning 17 CERT~C categories, while the other tools cover 6--15 on
this benchmark.  A hypothetical ensemble of all five tools would
achieve both high per-CWE precision and broad coverage.

\subsection{Real-World Results}

\Cref{tab:realworld-results} compares SqC against cppcheck and
clang-tidy.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrrr@{}}
\toprule
\textbf{Project} & \textbf{sqc} & \textbf{cppcheck} & \textbf{clang-tidy} \\
\midrule
libcrc & 734 & 43 & 2 \\
sqlite & 129,035 & 1,181 & 135 \\
mosquitto & 29,824 & 747 & 44 \\
curl & 63,207 & 519 & 114 \\
hostap & 179,833 & 2,118 & 2,279 \\
\midrule
\textbf{Total} & \textbf{402,633} & \textbf{4,608} & \textbf{2,574} \\
\bottomrule
\end{tabular}
\caption{Violation counts (sqc v0.3.5, cppcheck v2.10, clang-tidy
v21.1.6).  The 100$\times$ difference reflects SqC's 290-rule coverage
vs.~$\sim$20 checks in competitors.}
\label{tab:realworld-results}
\end{table}

\Cref{fig:realworld-reduction} shows that FP reductions developed on
Juliet transfer to real-world code: total violations across all five
codebases dropped 73.8\% from v0.2.7 to v0.3.55.

\begin{figure}[t]
\centering
\begin{tikzpicture}
\begin{axis}[
    width=\columnwidth,
    height=5cm,
    ybar,
    xlabel={SqC Version},
    ylabel={Total Violations (K)},
    ymin=0, ymax=620,
    xtick=data,
    xticklabels={v0.2.7, v0.2.13, v0.2.21, v0.3.5, v0.3.55},
    xticklabel style={rotate=45, anchor=east, font=\footnotesize},
    ylabel style={font=\footnotesize},
    xlabel style={font=\footnotesize},
    tick label style={font=\footnotesize},
    bar width=12pt,
    nodes near coords,
    nodes near coords style={font=\tiny, rotate=90, anchor=west},
    ymajorgrids=true,
    grid style={dashed, gray!30},
]
\addplot[fill=blue!40, draw=blue!60] coordinates {
    (1, 548.0)
    (2, 462.3)
    (3, 410.9)
    (4, 402.6)
    (5, 143.4)
};
\end{axis}
\end{tikzpicture}
\caption{Total SqC violations across 5 real-world codebases by
version.  Reduction: $-$404,600 ($-$73.8\%).}
\label{fig:realworld-reduction}
\end{figure}

For the one rule where all three tools have overlapping coverage
(null pointer dereference), the difference in analysis philosophy is
stark.  On mosquitto, SqC reports 8,657 EXP34-C violations compared
to cppcheck's 2 \texttt{nullPointer} findings.  cppcheck fires only
when it can \emph{prove} a null dereference path exists; SqC flags all
paths that \emph{may} dereference a null pointer.  This is a
fundamental tradeoff between coverage and precision that different users
will weight differently.

\subsection{Runtime Performance}
\label{sec:runtime}

To characterize SqC's runtime behavior across hardware configurations,
we ran single-process scans (cross-file analysis enabled, no prescan
cache) on four machines (\cref{tab:testbed}).

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}clccc@{}}
\toprule
\textbf{ID} & \textbf{CPU} & \textbf{Cores} & \textbf{RAM} & \textbf{Disk} \\
\midrule
M1 & Xeon E5-2640 @ 2.50\,GHz & 6/24 & 192\,GB & SSD \\
M2 & Ryzen 5 PRO 2400GE @ 3.20\,GHz & 4/8  & 16\,GB  & SSD \\
M3 & i7-7700T @ 2.90\,GHz    & 4/8  & 16\,GB  & SSD \\
M4 & i7-9750H @ 2.60\,GHz    & 6/12 & 16\,GB  & HDD \\
\bottomrule
\end{tabular}
\caption{Test machines.  Cores listed as physical/logical.
All run Linux 6.x.  M1 is a rack server; M2--M4 are desktop/laptop
class.}
\label{tab:testbed}
\end{table}

\Cref{tab:runtime} shows wall-clock times for single-process analysis
of sqlite (404K LOC) across all four machines with cross-file analysis
enabled and no prescan cache (cold start).

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}crr@{}}
\toprule
\textbf{Machine} & \textbf{Time (s)} & \textbf{LOC/s} \\
\midrule
M1 & 3{,}470 & 117 \\
M2 & 3{,}116 & 130 \\
M3 & 2{,}363 & 171 \\
M4 & 1{,}986 & 204 \\
\bottomrule
\end{tabular}
\caption{Single-process analysis time for sqlite (404,701 LOC,
cross-file analysis enabled, cold cache).}
\label{tab:runtime}
\end{table}

\textbf{Scaling across codebases.}
\Cref{tab:runtime-scaling} shows how throughput varies with codebase
size on M1.  Throughput generally decreases for larger codebases
because cross-file analysis cost grows with the number of
inter-procedural edges.  The exception is sqlite, whose throughput
(117 LOC/s) is lower than the larger hostap (155 LOC/s).  This is
because sqlite ships as a single 230K-line amalgamation file
(\texttt{sqlite3.c}) with dense internal cross-references, whereas
hostap distributes its code across 504 files averaging $\sim$1{,}070
LOC each.  Per-file analysis cost is dominated by intra-file
complexity, not total codebase size.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}lrrr@{}}
\toprule
\textbf{Project} & \textbf{LOC} & \textbf{Time (s)} & \textbf{LOC/s} \\
\midrule
libcrc    &   2{,}130 &     2 &   888 \\
mosquitto &  88{,}717 &   172 &   516 \\
curl      & 240{,}331 &   609 &   395 \\
hostap    & 539{,}617 & 3{,}479 & 155 \\
sqlite    & 404{,}701 & 3{,}470 & 117 \\
\bottomrule
\end{tabular}
\caption{Single-process throughput on M1 across all five benchmark
codebases (cross-file analysis, cold cache).  sqlite's lower
throughput despite fewer LOC than hostap reflects the cost of
analyzing its 230K-line amalgamation file.}
\label{tab:runtime-scaling}
\end{table}

In single-process mode, loading a cached prescan saves less than 1\%
of wall time---the prescan is a small fraction of total analysis cost.
The cache's primary benefit is enabling parallel analysis.

\textbf{Parallel mode.}  SqC provides built-in file-level parallelism
via the \texttt{-j}/\texttt{-{}-jobs} flag (default: auto-detect CPU
count).  Each worker thread receives its own parser and rule registry
instance, avoiding shared mutable state.  After a single-threaded
prescan builds the cross-file context (function summaries, call graph,
null states), files are analyzed in parallel with results merged
deterministically.

\textbf{LPT scheduling.}  Real-world C codebases exhibit highly skewed
file-size distributions: curl's largest file (169\,KB) is 44$\times$
the median, and sqlite's amalgamation file (630\,KB) is 37$\times$ the
median.  Na\"ive parallel dispatch can leave threads idle while a
straggler processes a large file.  SqC applies Graham's
Longest-Processing-Time-first (LPT) heuristic~\cite{graham-lpt}: files
are sorted by size in descending order and dispatched via demand-driven
scheduling, where each thread pulls the next unprocessed file upon
becoming idle.  This achieves a $(4/3 - 1/3m)$-approximation to the
optimal makespan, where $m$ is the thread count.

\Cref{tab:parallel} shows the impact on a 24-core server.  LPT
scheduling reduces worst-case latency by 6--9\% and, critically,
reduces run-to-run variance by 52--99\%, making analysis times
predictable for CI/CD integration.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}llrrrr@{}}
\toprule
\textbf{Project} & \textbf{Mode} & \textbf{Avg (s)} & \textbf{Worst (s)} & \textbf{$\sigma$ (s)} & \textbf{CPU eff.} \\
\midrule
curl    & par\_iter (baseline) & 178.5 & 193.4 & 13.0 & --- \\
curl    & LPT + par\_bridge   & 176.4 & 176.5 &  0.1 & --- \\
\addlinespace
sqlite  & par\_iter (baseline) & 1{,}197 & 1{,}280 & 71.8 & 83\% \\
sqlite  & LPT + par\_bridge   & 1{,}169 & 1{,}204 & 34.3 & 96\% \\
\bottomrule
\end{tabular}
\caption{LPT scheduling impact at $-j\,4$ (3 runs each, cross-file
analysis enabled).  CPU efficiency is CPU time divided by wall time
divided by thread count; 100\% means zero idle time.  LPT's primary
benefit is variance reduction and worst-case improvement, not average
speedup.}
\label{tab:parallel}
\end{table}

The Juliet benchmark (70 CWEs, fast mode) completes in approximately
15 minutes on the 24-core server using per-CWE parallelism.

%% ============================================================
\section{Iterative False Positive Reduction}
\label{sec:fp-reduction}

Reducing false positives while maintaining detection is the central
engineering challenge for a broad-coverage static analyzer.  This
section walks through the techniques that improved the TP rate from
41.1\% to 67.5\%, organized by the insight that motivated each change.

\Cref{fig:fp-reduction} shows the TP and FP counts across 12
optimization rounds.

\begin{figure}[t]
\centering
\begin{tikzpicture}
\begin{axis}[
    width=\columnwidth,
    height=5.5cm,
    xlabel={Optimization Round},
    ylabel={Count (thousands)},
    ymin=100, ymax=900,
    xmin=0, xmax=13,
    xtick={1,2,3,4,5,6,7,8,9,10,11,12},
    ylabel style={font=\footnotesize},
    xlabel style={font=\footnotesize},
    tick label style={font=\footnotesize},
    grid=major,
    grid style={dashed, gray!30},
    legend style={font=\tiny, at={(0.97,0.97)}, anchor=north east},
]
\addplot[color=red, mark=triangle*, thick] coordinates {
    (0, 839.3)
    (1, 752.4) (2, 736.6) (3, 537.6) (4, 492.6)
    (5, 475.8) (6, 327.2) (7, 301.5) (8, 296.4)
    (9, 243.8) (10, 239.7) (11, 215.7) (12, 161.5)
};
\addlegendentry{FP}
\addplot[color=blue, mark=*, thick] coordinates {
    (0, 586.5)
    (1, 552.6) (2, 555.7) (3, 402.0) (4, 363.9)
    (5, 340.9) (6, 247.8) (7, 231.1) (8, 231.0)
    (9, 189.9) (10, 189.0) (11, 172.8) (12, 130.2)
};
\addlegendentry{TP}
\end{axis}
\end{tikzpicture}
\caption{TP and FP counts across optimization rounds (full Juliet
suite, thousands).  Both decline as overly-broad rules are tightened;
the key metric is TP rate, which improved from 41.1\% to 44.6\%.}
\label{fig:fp-reduction}
\end{figure}

\subsection{The Header Problem}
\label{sec:fp-cross-file}

The first and largest source of false positives came from a fundamental
limitation of the tree-sitter approach: without \texttt{\#include}
resolution, every call to a function defined in another file looks like
a call to an undeclared function.  Rules DCL31-C and DCL07-C would flag
every \texttt{malloc()}, \texttt{printf()}, and \texttt{CreateFile()}
call.

The fix came in two stages.  First, a built-in database of standard
functions (Round 3, $-$198,974 FPs).  Second, cross-file analysis: a
pre-scan of project directories to discover all function definitions
(Round 6, $-$148,622 FPs).  Together, these two changes account for
41\% of the total FP reduction.

Later, adding $\sim$100 Windows API functions to the database
(Round 9) removed another 36K FPs from DCL31-C/DCL07-C alone---the
biggest single-rule win of any round.

\subsection{Type-Aware Analysis}

Several integer rules initially operated on the text of expressions
without considering the types of the operands.  This produced false
positives on non-integer types:

\begin{itemize}
    \item \texttt{char data; data + 1} was flagged as signed integer
        overflow (INT32-C).  Fix: \texttt{infer\_type()} returns
        ``not applicable'' for non-integer types.
    \item \texttt{ptr + n} was flagged as unsigned integer wrap
        (INT30-C).  Fix: pointer arithmetic is identified as valid C.
    \item Float-to-int assignments with unknown types were flagged
        (FLP34-C).  Fix: only flag when both sides have known,
        mismatched types.
\end{itemize}

The lesson: \emph{don't flag when uncertain.}  Suppressing warnings
for unknown or ambiguous types consistently improved the FP-to-TP
ratio.

\subsection{Bounds-Check Detection}

INT32-C and INT30-C gained AST-ancestor walking to detect enclosing
bounds checks.  The algorithm walks up to 15 ancestors looking for an
enclosing \texttt{if\_statement} whose condition references a type-limit
macro (\texttt{INT\_MAX}, \texttt{UINT64\_MAX}, etc.) \emph{and} at
least one arithmetic operand.

This achieved a 12.5:1 FP-to-TP reduction ratio for INT32-C.  One
important implementation detail: we use word-boundary matching for
macro names, because \texttt{ULONG\_MAX} contains \texttt{LONG\_MAX}
as a substring.

\subsection{Constant Evaluation}

The constant evaluator resolves $\sim$35 C standard limit macros,
$\sim$20 type sizes via \texttt{sizeof}, and arithmetic on these
values.  This produced the largest single-round FP reduction since
cross-file analysis ($-$12,088 FPs in Round 12), because it enabled
every integer rule to reason about allocation sizes, buffer bounds,
and guard conditions.

\subsection{Diminishing Returns}

\Cref{tab:fp-milestones} shows selected milestones.  The pattern of
diminishing returns is clear: Round 3 removed 199K FPs; by Round 8,
only 5K.  Each successive round requires more sophisticated analysis
for smaller gains.

\begin{table}[t]
\centering
\footnotesize
\begin{tabular}{@{}rlrrr@{}}
\toprule
\textbf{Rd} & \textbf{Key Change} & \textbf{FP} & \textbf{$\Delta$FP} & \textbf{TP\%} \\
\midrule
0 & Baseline (full suite) & 839K & --- & 41.1 \\
3 & Std function DB & 538K & $-$199K & 42.8 \\
6 & Cross-file analysis & 327K & $-$149K & 43.1 \\
9 & Windows API + types & 244K & $-$53K & 43.8 \\
12 & Const eval + macros & 162K & $-$12K & 44.6 \\
\midrule
\multicolumn{5}{@{}l}{\emph{Switch to fast-mode benchmarking (v0.3.20)}} \\
\midrule
--- & Fast-mode baseline & 24.7K & --- & 45.8 \\
--- & VRA + buffer analysis & 18.8K & $-$5.9K & 56.3 \\
--- & CFG const + CWE-369 & 17.0K & $-$1.8K & 60.2 \\
--- & Taint + return-value   & 14.1K & $-$2.9K & 63.6 \\
v0.3.119 & Precision improvements & 11.7K & $-$2.4K & 67.5 \\
\bottomrule
\end{tabular}
\caption{FP reduction milestones.  Full-suite rounds 0--12 reduced FP by
80.7\%.  Fast-mode continued from 45.8\% to 67.5\% TP rate.  The v0.3.119
row reflects continued per-CWE precision improvements across 74 CWEs.}
\label{tab:fp-milestones}
\end{table}

\subsection{Principles That Emerged}

Four principles emerged from this iterative process:

\begin{enumerate}
    \item \textbf{Don't flag when uncertain.}  If the type, origin,
        or value of an operand is unknown, suppress the warning.
    \item \textbf{Target absolute FP counts, not rates.}  A rule with
        99\% FP rate and 100 violations matters less than one with
        50\% FP rate and 10K violations.
    \item \textbf{Zero-TP rules are free FP reductions.}  DCL40-C's
        31-character identifier prefix check produced 12K FPs and 0 TPs
        on Juliet.  Removing it was cost-free.
    \item \textbf{Verify on both benchmarks.}  Fixes targeting
        \texttt{uint32\_t} resolution show impact on real-world code
        but not on Juliet, which uses explicit C types.
\end{enumerate}

%% ============================================================
\section{Worked Example: Null Dereference Detection}
\label{sec:worked-example}

To make the analysis pipeline concrete, consider this C function:

\begin{lstlisting}
void process(FILE *log) {
    char *buf = malloc(256);
    if (buf == NULL) {
        fprintf(log, "alloc failed\n");
        return;
    }
    /* buf is NotNull here */
    strcpy(buf, "initialized");
    free(buf);
}
\end{lstlisting}

SqC's analysis proceeds through the four stages:

\textbf{Stage 1 (Parse):} Tree-sitter produces a CST.  The
\texttt{malloc} call, \texttt{if} statement, \texttt{return},
\texttt{strcpy}, and \texttt{free} are all represented as nodes.

\textbf{Stage 2 (Prescan):} If cross-file analysis is enabled,
\texttt{malloc} is recognized from the standard function database
as a function that may return NULL.

\textbf{Stage 3 (CFG + Dataflow):} The CFG has four basic blocks:
entry (malloc call), the null-check branch, the error path (fprintf +
return), and the success path (strcpy + free).  The null-state dataflow
computes:
\begin{itemize}
    \item After \texttt{malloc}: \texttt{buf} $=$ PossiblyNull
    \item True branch (\texttt{buf == NULL}): \texttt{buf} $=$ DefinitelyNull
    \item False branch: \texttt{buf} $=$ NotNull
    \item At \texttt{strcpy}: \texttt{buf} $=$ NotNull (the DefinitelyNull
        path returned early)
\end{itemize}

\textbf{Stage 4 (Rule Evaluation):} EXP34-C checks every dereference
of a pointer.  At the \texttt{strcpy} call, \texttt{buf} is NotNull,
so no violation is reported.  If the \texttt{if (buf == NULL)} check
were removed, \texttt{buf} would still be PossiblyNull at \texttt{strcpy},
and EXP34-C would flag the violation.

%% ============================================================
\section{Limitations}
\label{sec:limitations}

SqC has several architectural limitations that cap its achievable
precision:

\textbf{No preprocessor expansion.}  Macros appear as identifier
tokens or function calls.  SqC cannot determine whether
\texttt{SAFE\_FREE(p)} performs a null check internally.

\textbf{No alias analysis.}  Pointer aliasing is unresolved.  If
\texttt{p} and \texttt{q} point to the same memory and \texttt{q}
is checked for null, SqC does not propagate that information to
\texttt{p}.

\textbf{No symbolic execution.}  SqC's value-range analysis handles
constant expressions but cannot reason about symbolic relationships
(e.g., that \texttt{n} is always less than \texttt{sizeof(buf)}).

\textbf{No SSA form.}  Without static single assignment, use-def
chains are approximate, limiting the precision of dataflow analyses.

These gaps are fundamental to the tree-sitter approach.  A tool built
on Clang's AST would resolve macros and includes automatically but
would require a compilation database.  The current architecture---AST
plus CFG plus null-state dataflow plus value-range analysis plus
inter-procedural summaries (including cross-function taint
propagation)---has reached 67.5\% TP rate, exceeding the previously
estimated 65\% ceiling.  Further improvements likely require alias
analysis or symbolic execution to push beyond approximately 70\%.

The 6 remaining CWEs with zero detection (\cref{tab:precision-tiers})
reveal residual coverage gaps.  CWE-259 (Hard-Coded Password) is
blocked by tree-sitter's inability to expand preprocessor macros in
Juliet's Windows headers; CWE-328 (Reversible One-Way Hash) requires
a new cryptographic algorithm detection rule; the remainder (CWE-23,
CWE-672, CWE-676, CWE-762) await dedicated rule implementations.

%% ============================================================
\section{Related Work}
\label{sec:related}

\textbf{Commercial tools.}
Coverity~\cite{coverity}, Klocwork, and Polyspace use inter-procedural
dataflow, symbolic execution, and abstract interpretation, generally
achieving lower false positive rates than SqC on comparable checks.
However, their closed-source nature prevents independent benchmarking,
and licensing costs limit adoption.

\textbf{cppcheck}~\cite{cppcheck} implements $\sim$20 CERT C checks
with low false positive rates.  On our benchmarks, cppcheck's
\texttt{nullPointer} check finds 2 violations on mosquitto compared
to SqC's 8,657---cppcheck requires proof while SqC flags potential
paths.

\textbf{clang-tidy}~\cite{clang-tidy} implements $\sim$20 CERT C
checks via its \texttt{cert-*} module.  It benefits from Clang's full
semantic analysis but requires a compilation database.

\textbf{Infer}~\cite{infer} uses bi-abduction and separation logic
for memory safety.  On the Juliet benchmark (\cref{tab:competitor}),
Infer achieves higher precision on null dereference and double-free
but lower precision on buffer overflow CWEs.  It covers $\sim$10
defect classes vs.\ SqC's 118.

\textbf{Frama-C}~\cite{frama-c} provides formal verification via
abstract interpretation (EVA plugin).  On overlapping CWEs
(\cref{tab:competitor}), Frama-C achieves the highest per-CWE
precision, including 100\% on numeric truncation.  However, it
requires per-function entry points, takes $\sim$6$\times$ longer per
file, and covers 6 CWEs here vs.\ SqC's 118.

The coverage gap is notable: Kaner~\cite{kaner} argues that 100\%
coverage of any single metric is misleading---what matters is
reasonable coverage across \emph{many} dimensions.
Marick~\cite{marick} demonstrates that code coverage tools are
most useful as guides for what to test next, not as proof of
correctness.  SqC's approach of implementing 290 rules (broad
coverage) rather than perfecting a handful aligns with this
philosophy.

%% ============================================================
\section{Conclusion}
\label{sec:conclusion}

We have presented SqC, a static analysis tool implementing 290 CERT C
rules across 17 categories.  Through tree-sitter-based parsing,
control-flow graph construction, null-state dataflow analysis,
value-range analysis, and inter-procedural function summaries
(including cross-function taint propagation), SqC achieves a 67.5\%
true positive rate on the Juliet benchmark with 34 CWEs at 100\%
precision.  On a five-tool comparison, SqC surpasses Frama-C
(62.5\% vs.\ 61.0\% on overlapping CWEs) while covering 5--12$\times$
more vulnerability categories.

On real-world codebases totaling 1.27 million lines of code, total
violations were reduced 73.8\% through iterative refinement.  SqC
detects vulnerability classes that existing open-source tools do not
cover, including POSIX thread misuse, integer overflow, and
inter-procedural null dereference.

The tool's primary limitation---its tree-sitter foundation---is also
its key advantage: SqC analyzes any C file without a build system,
compilation database, or preprocessing step.  For teams that need
broad CERT C coverage with minimal integration effort, this tradeoff
is worthwhile.

Future work includes alias analysis for pointer-related rules, further
expanding the cross-function taint infrastructure --- CWE-78 (OS command
injection) now achieves 100\% precision via caller-aware ENV03-C suppression
and return-value propagation, and the same summary bits extend naturally
to CWE-89 sinks (\texttt{sqlite3\_exec}, \texttt{mysql\_query},
\texttt{PQexec}) and format-string injection --- and further closing the
precision gap with clang-tidy on buffer overflow CWEs through improved
buffer-size inference.

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