llvm dev - Jun 2020 - Introducing the binary-level coverage analysis tool bcov

## TL;DR

We introduce bcov, an open-source binary-level coverage analysis tool [1].
The details are discussed in our paper [2], which is accepted to
ESEC/FSE'20. bcov statically instruments x86-64 ELF binaries without
compiler support. It features several techniques that allow it to achieve
high performance, transparency, and flexibility.

For example, running "make check-llvm-codegen" with an instrumented
version
of llc introduces a mean performance overhead of about 32%. The overhead
for smaller binaries can be as low as 2%. bcov accurately reports code
coverage with a mean F-score of 99,86%. All of that without introducing any
test regressions.


## Design overview

Given an ELF module as input, bcov will first analyze it to choose
appropriate probe locations and estimate the size of the program segments
required for patching. This analysis depends on the instrumentation policy
chosen by the user. Then, bcov patches the input module by adding two
program segments; one segment contains trampoline code and the other keeps
coverage data.

bcov uses trampolines to instrument the binary where it inserts probes in
targeted locations to track basic block coverage. Each probe consists of a
detour that diverts control flow to a designated trampoline. The trampoline
updates coverage data using a single pc-relative mov instruction (e.g., mov
BYTE PTR [rip+0xadd88],1), executes relocated instructions, and then
restores control flow to its original state.

To dump coverage data, the user can inject our small runtime library,
libbcov-rt, using the LD_PRELOAD mechanism. This library scans the memory
mappings of the current process to dump every data segment that starts with
a specific magic number. This means that coverage data will be dumped
separately for each ELF module. Dumping data takes place at process
shutdown or upon receiving a user signal. The latter feature enables online
coverage tracking.


## Key features

bcov integrates several techniques including:

 - Probe pruning. We instrument only what is necessary for tracking code
coverage. To this end, we bring Agrawal's probe pruning technique [3] to
binary-level instrumentation.

 - Precise CFG analysis. Imprecision in the recovered CFG can have several
effects, which include causing the instrumented binary to crash. To improve
CFG precision, we propose a novel jump table analysis and implement a
variant of an available non-return analysis [4].

 - Static instrumentation. We implement several techniques like (1)
optimized probe selection, (2) jump table instrumentation, (3) and
systematic detour hosting.

The result is that our approach can work transparently, and with low
overhead, on large and well-tested C and C++ programs.


## Relation to the compiler

Code coverage analysis at the binary level, as implemented in bcov, can
offer several benefits: first, it is highly flexible. A user can analyze
the coverage of a particular module only. Even better, coverage tracking
can be limited to specific functions, e.g., the ones affected by recent
changes. Second, it is largely independent of the used compiler and the
build system. We observed consistent results after experimenting with four
different versions of gcc and clang and three different build types,
namely, debug, release, and LTO. Finally, the primitives we use to divert
control-flow and update coverage are simple. One can imagine (and hope)
that extending bcov to system code, or other native languages, will not
require a lot of work.

However, the viability of static binary-level coverage analysis ultimately
depends on the compiler toolchain that produces the binary. Specifically,
we can think of two areas where compilers might help our tool by adding
extra support, or at least by not emitting code that is difficult to
analyze:

 - Source-level coverage. The ability to know whether a particular basic
block was covered in the binary is quite interesting. However, developers
would ultimately want to map this coverage information to source-level
artifacts. Statement coverage can be supported already with the help of
dwarf debugging information. But bcov can report the coverage of individual
branch decisions at the binary level. Therefore, leveraging this
information to obtain source-level MC/DC seems to be a natural next step.
Ideally, we want to infer MC/DC using what we have already, namely,
binary-level coverage and dwarf information. However, adding support to a
custom mapping format similar to the one available in llvm-cov might be
necessary. In this regard, we appreciate your suggestions on the best way
to support MC/DC, if at all feasible. (feedback needed)

 - Function model. We assume that a function occupies a continuous code
region. The function's main entry is expected to be at the beginning of
this region. Also, we consider each landing pad to be a function entry as
well. Based on this, we assume that the identified entries predominate all
basic blocks inside the function. In other words, a function can not reach
an arbitrary basic block inside another function without first visiting one
of its entries. This assumption seems to hold pretty well in the binaries
we experimented with, but we are not sure to what extent it generalizes to
other compilers and ISAs. (feedback needed)


## Potential future work

Updating coverage using a single pc-relative mov instruction is transparent
in the sense that it does not clobber any general-purpose register. It even
preserves the CPU's eflags. This, in turn, has greatly simplified the
implementation of our prototype. That is, we did not have to think about
saving/restoring the CPU state. However, putting mechanisms in place to
allow eflags to be *safely* clobbered, e.g., liveness analysis, can open
the door for several design alternatives where a pc-relative mov (e.g., mov
BYTE PTR [rip+0xadd88],1) can be replaced with:

  -  or BYTE PTR [rip+0xadd88],c: where c is one-hot constant. This will
reduce the size of required coverage data to 1/8 of the size currently
required by bcov.

  - add BYTE PTR [rip+0xadd88],1: 8-bit counter per superblock (or selected
set of basic blocks). This might be useful for fuzzing.

  - add DWORD PTR [rip+0xadd88],1: 32-bit counter per superblock (or
selected set of basic blocks). This can be useful for profiling.


So there is significant potential here. However, we have not explored these
ideas any further, and we would highly appreciate any feedback about their
viability and potential added value in comparison to what is already
available in llvm-cov and sancov. (feedback needed)

To conclude, we introduced bcov [1] and discussed a few areas where the
LLVM community can help us assess its potential, identify related work, and
plan its future direction. Many thanks in advance for your feedback.


Kind regards,

Ammar


 [1]: https://github.com/abenkhadra/bcov
 [2]: https://arxiv.org/pdf/2004.14191.pdf
 [3]: https://dl.acm.org/doi/10.1145/174675.175935
 [4]: https://dl.acm.org/doi/10.1145/2931037.2931047
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Hi Ammar, bcov sounds really interesting.

I just completed implementation for LLVM source-based branch condition coverage,
which extends llvm-cov visualization and makes use of (mostly) existing counter
instrumentation to track True/False execution counts for leaf-conditions
comprising any source-level Boolean expression.  I plan to upstream this support
soon.  MC/DC would also be the next logical step.

Having a binary-level coverage tool is a great complement, and perhaps you can
utilize the extensions I made llvm-cov for visualization.

-Alan Phipps

From: llvm-dev [mailto:llvm-dev-bounces at lists.llvm.org] On Behalf Of Ammar
Ben Khadra via llvm-dev
Sent: Friday, June 26, 2020 4:51 AM
To: llvm-dev list
Subject: [EXTERNAL] [llvm-dev] Introducing the binary-level coverage analysis
tool bcov


## TL;DR

We introduce bcov, an open-source binary-level coverage analysis tool [1]. The
details are discussed in our paper [2], which is accepted to ESEC/FSE'20.
bcov statically instruments x86-64 ELF binaries without compiler support. It
features several techniques that allow it to achieve high performance,
transparency, and flexibility.

For example, running "make check-llvm-codegen" with an instrumented
version of llc introduces a mean performance overhead of about 32%. The overhead
for smaller binaries can be as low as 2%. bcov accurately reports code coverage
with a mean F-score of 99,86%. All of that without introducing any test
regressions.


## Design overview

Given an ELF module as input, bcov will first analyze it to choose appropriate
probe locations and estimate the size of the program segments required for
patching. This analysis depends on the instrumentation policy chosen by the
user. Then, bcov patches the input module by adding two program segments; one
segment contains trampoline code and the other keeps coverage data.

bcov uses trampolines to instrument the binary where it inserts probes in
targeted locations to track basic block coverage. Each probe consists of a
detour that diverts control flow to a designated trampoline. The trampoline
updates coverage data using a single pc-relative mov instruction (e.g., mov BYTE
PTR [rip+0xadd88],1), executes relocated instructions, and then restores control
flow to its original state.

To dump coverage data, the user can inject our small runtime library,
libbcov-rt, using the LD_PRELOAD mechanism. This library scans the memory
mappings of the current process to dump every data segment that starts with a
specific magic number. This means that coverage data will be dumped separately
for each ELF module. Dumping data takes place at process shutdown or upon
receiving a user signal. The latter feature enables online coverage tracking.


## Key features

bcov integrates several techniques including:

 - Probe pruning. We instrument only what is necessary for tracking code
coverage. To this end, we bring Agrawal's probe pruning technique [3] to
binary-level instrumentation.

 - Precise CFG analysis. Imprecision in the recovered CFG can have several
effects, which include causing the instrumented binary to crash. To improve CFG
precision, we propose a novel jump table analysis and implement a variant of an
available non-return analysis [4].

 - Static instrumentation. We implement several techniques like (1) optimized
probe selection, (2) jump table instrumentation, (3) and systematic detour
hosting.

The result is that our approach can work transparently, and with low overhead,
on large and well-tested C and C++ programs.


## Relation to the compiler

Code coverage analysis at the binary level, as implemented in bcov, can offer
several benefits: first, it is highly flexible. A user can analyze the coverage
of a particular module only. Even better, coverage tracking can be limited to
specific functions, e.g., the ones affected by recent changes. Second, it is
largely independent of the used compiler and the build system. We observed
consistent results after experimenting with four different versions of gcc and
clang and three different build types, namely, debug, release, and LTO. Finally,
the primitives we use to divert control-flow and update coverage are simple. One
can imagine (and hope) that extending bcov to system code, or other native
languages, will not require a lot of work.

However, the viability of static binary-level coverage analysis ultimately
depends on the compiler toolchain that produces the binary. Specifically, we can
think of two areas where compilers might help our tool by adding extra support,
or at least by not emitting code that is difficult to analyze:

 - Source-level coverage. The ability to know whether a particular basic block
was covered in the binary is quite interesting. However, developers would
ultimately want to map this coverage information to source-level artifacts.
Statement coverage can be supported already with the help of dwarf debugging
information. But bcov can report the coverage of individual branch decisions at
the binary level. Therefore, leveraging this information to obtain source-level
MC/DC seems to be a natural next step. Ideally, we want to infer MC/DC using
what we have already, namely, binary-level coverage and dwarf information.
However, adding support to a custom mapping format similar to the one available
in llvm-cov might be necessary. In this regard, we appreciate your suggestions
on the best way to support MC/DC, if at all feasible. (feedback needed)

 - Function model. We assume that a function occupies a continuous code region.
The function's main entry is expected to be at the beginning of this region.
Also, we consider each landing pad to be a function entry as well. Based on
this, we assume that the identified entries predominate all basic blocks inside
the function. In other words, a function can not reach an arbitrary basic block
inside another function without first visiting one of its entries. This
assumption seems to hold pretty well in the binaries we experimented with, but
we are not sure to what extent it generalizes to other compilers and ISAs.
(feedback needed)


## Potential future work

Updating coverage using a single pc-relative mov instruction is transparent in
the sense that it does not clobber any general-purpose register. It even
preserves the CPU's eflags. This, in turn, has greatly simplified the
implementation of our prototype. That is, we did not have to think about
saving/restoring the CPU state. However, putting mechanisms in place to allow
eflags to be *safely* clobbered, e.g., liveness analysis, can open the door for
several design alternatives where a pc-relative mov (e.g., mov BYTE PTR
[rip+0xadd88],1) can be replaced with:

  -  or BYTE PTR [rip+0xadd88],c: where c is one-hot constant. This will reduce
the size of required coverage data to 1/8 of the size currently required by
bcov.

  - add BYTE PTR [rip+0xadd88],1: 8-bit counter per superblock (or selected set
of basic blocks). This might be useful for fuzzing.

  - add DWORD PTR [rip+0xadd88],1: 32-bit counter per superblock (or selected
set of basic blocks). This can be useful for profiling.


So there is significant potential here. However, we have not explored these
ideas any further, and we would highly appreciate any feedback about their
viability and potential added value in comparison to what is already available
in llvm-cov and sancov. (feedback needed)

To conclude, we introduced bcov [1] and discussed a few areas where the LLVM
community can help us assess its potential, identify related work, and plan its
future direction. Many thanks in advance for your feedback.


Kind regards,

Ammar


 [1]: https://github.com/abenkhadra/bcov
 [2]: https://arxiv.org/pdf/2004.14191.pdf
 [3]: https://dl.acm.org/doi/10.1145/174675.175935
 [4]: https://dl.acm.org/doi/10.1145/2931037.2931047

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Hi Alan, thanks for pointing that out! I was not aware of this ongoing work.

I will have a deeper look at it to see how it can be complemented with
binary-level coverage. Thanks again.

- Ammar


On Fri, Jun 26, 2020 at 10:41 PM Phipps, Alan <a-phipps at ti.com> wrote:
> Hi Ammar, bcov sounds really interesting.
>
>
>
> I just completed implementation for LLVM source-based branch condition
> coverage, which extends llvm-cov visualization and makes use of (mostly)
> existing counter instrumentation to track True/False execution counts for
> leaf-conditions comprising any source-level Boolean expression.  I plan to
> upstream this support soon.  MC/DC would also be the next logical step.
>
>
>
> Having a binary-level coverage tool is a great complement, and perhaps you
> can utilize the extensions I made llvm-cov for visualization.
>
>
>
> -Alan Phipps
>
>
>
> *From:* llvm-dev [mailto:llvm-dev-bounces at lists.llvm.org] *On Behalf Of
*Ammar
> Ben Khadra via llvm-dev
> *Sent:* Friday, June 26, 2020 4:51 AM
> *To:* llvm-dev list
> *Subject:* [EXTERNAL] [llvm-dev] Introducing the binary-level coverage
> analysis tool bcov
>
>
>
>
>
> ## TL;DR
>
> We introduce bcov, an open-source binary-level coverage analysis tool [1].
> The details are discussed in our paper [2], which is accepted to
> ESEC/FSE'20. bcov statically instruments x86-64 ELF binaries without
> compiler support. It features several techniques that allow it to achieve
> high performance, transparency, and flexibility.
>
>
>
> For example, running "make check-llvm-codegen" with an
instrumented
> version of llc introduces a mean performance overhead of about 32%. The
> overhead for smaller binaries can be as low as 2%. bcov accurately reports
> code coverage with a mean F-score of 99,86%. All of that without
> introducing any test regressions.
>
>
>
> ## Design overview
>
> Given an ELF module as input, bcov will first analyze it to choose
> appropriate probe locations and estimate the size of the program segments
> required for patching. This analysis depends on the instrumentation policy
> chosen by the user. Then, bcov patches the input module by adding two
> program segments; one segment contains trampoline code and the other keeps
> coverage data.
>
> bcov uses trampolines to instrument the binary where it inserts probes in
> targeted locations to track basic block coverage. Each probe consists of a
> detour that diverts control flow to a designated trampoline. The trampoline
> updates coverage data using a single pc-relative mov instruction (e.g., mov
> BYTE PTR [rip+0xadd88],1), executes relocated instructions, and then
> restores control flow to its original state.
>
> To dump coverage data, the user can inject our small runtime library,
> libbcov-rt, using the LD_PRELOAD mechanism. This library scans the memory
> mappings of the current process to dump every data segment that starts with
> a specific magic number. This means that coverage data will be dumped
> separately for each ELF module. Dumping data takes place at process
> shutdown or upon receiving a user signal. The latter feature enables online
> coverage tracking.
>
>
> ## Key features
>
> bcov integrates several techniques including:
>
>  - Probe pruning. We instrument only what is necessary for tracking code
> coverage. To this end, we bring Agrawal's probe pruning technique [3]
to
> binary-level instrumentation.
>
>  - Precise CFG analysis. Imprecision in the recovered CFG can have several
> effects, which include causing the instrumented binary to crash. To improve
> CFG precision, we propose a novel jump table analysis and implement a
> variant of an available non-return analysis [4].
>
>  - Static instrumentation. We implement several techniques like (1)
> optimized probe selection, (2) jump table instrumentation, (3) and
> systematic detour hosting.
>
> The result is that our approach can work transparently, and with low
> overhead, on large and well-tested C and C++ programs.
>
>
> ## Relation to the compiler
>
> Code coverage analysis at the binary level, as implemented in bcov, can
> offer several benefits: first, it is highly flexible. A user can analyze
> the coverage of a particular module only. Even better, coverage tracking
> can be limited to specific functions, e.g., the ones affected by recent
> changes. Second, it is largely independent of the used compiler and the
> build system. We observed consistent results after experimenting with four
> different versions of gcc and clang and three different build types,
> namely, debug, release, and LTO. Finally, the primitives we use to divert
> control-flow and update coverage are simple. One can imagine (and hope)
> that extending bcov to system code, or other native languages, will not
> require a lot of work.
>
> However, the viability of static binary-level coverage analysis ultimately
> depends on the compiler toolchain that produces the binary. Specifically,
> we can think of two areas where compilers might help our tool by adding
> extra support, or at least by not emitting code that is difficult to
> analyze:
>
>  - Source-level coverage. The ability to know whether a particular basic
> block was covered in the binary is quite interesting. However, developers
> would ultimately want to map this coverage information to source-level
> artifacts. Statement coverage can be supported already with the help of
> dwarf debugging information. But bcov can report the coverage of individual
> branch decisions at the binary level. Therefore, leveraging this
> information to obtain source-level MC/DC seems to be a natural next step.
> Ideally, we want to infer MC/DC using what we have already, namely,
> binary-level coverage and dwarf information. However, adding support to a
> custom mapping format similar to the one available in llvm-cov might be
> necessary. In this regard, we appreciate your suggestions on the best way
> to support MC/DC, if at all feasible. (feedback needed)
>
>  - Function model. We assume that a function occupies a continuous code
> region. The function's main entry is expected to be at the beginning of
> this region. Also, we consider each landing pad to be a function entry as
> well. Based on this, we assume that the identified entries predominate all
> basic blocks inside the function. In other words, a function can not reach
> an arbitrary basic block inside another function without first visiting one
> of its entries. This assumption seems to hold pretty well in the binaries
> we experimented with, but we are not sure to what extent it generalizes to
> other compilers and ISAs. (feedback needed)
>
>
> ## Potential future work
>
> Updating coverage using a single pc-relative mov instruction is
> transparent in the sense that it does not clobber any general-purpose
> register. It even preserves the CPU's eflags. This, in turn, has
greatly
> simplified the implementation of our prototype. That is, we did not have to
> think about saving/restoring the CPU state. However, putting mechanisms in
> place to allow eflags to be *safely* clobbered, e.g., liveness analysis,
> can open the door for several design alternatives where a pc-relative mov
> (e.g., mov BYTE PTR [rip+0xadd88],1) can be replaced with:
>
>   -  or BYTE PTR [rip+0xadd88],c: where c is one-hot constant. This will
> reduce the size of required coverage data to 1/8 of the size currently
> required by bcov.
>
>   - add BYTE PTR [rip+0xadd88],1: 8-bit counter per superblock (or
> selected set of basic blocks). This might be useful for fuzzing.
>
>   - add DWORD PTR [rip+0xadd88],1: 32-bit counter per superblock (or
> selected set of basic blocks). This can be useful for profiling.
>
>
> So there is significant potential here. However, we have not explored
> these ideas any further, and we would highly appreciate any feedback about
> their viability and potential added value in comparison to what is already
> available in llvm-cov and sancov. (feedback needed)
>
> To conclude, we introduced bcov [1] and discussed a few areas where the
> LLVM community can help us assess its potential, identify related work, and
> plan its future direction. Many thanks in advance for your feedback.
>
>
> Kind regards,
>
> Ammar
>
>
>  [1]: https://github.com/abenkhadra/bcov
>  [2]: https://arxiv.org/pdf/2004.14191.pdf
>  [3]: https://dl.acm.org/doi/10.1145/174675.175935
>  [4]: https://dl.acm.org/doi/10.1145/2931037.2931047
>
>
>
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On 2020-06-26, Ammar Ben Khadra via llvm-dev wrote:
>## TL;DR
>
>We introduce bcov, an open-source binary-level coverage analysis tool [1].
>The details are discussed in our paper [2], which is accepted to
>ESEC/FSE'20. bcov statically instruments x86-64 ELF binaries without
>compiler support. It features several techniques that allow it to achieve
>high performance, transparency, and flexibility.
>
>For example, running "make check-llvm-codegen" with an
instrumented version
>of llc introduces a mean performance overhead of about 32%. The overhead
>for smaller binaries can be as low as 2%. bcov accurately reports code
>coverage with a mean F-score of 99,86%. All of that without introducing any
>test regressions.
>
>
>## Design overview
>
>Given an ELF module as input, bcov will first analyze it to choose
>appropriate probe locations and estimate the size of the program segments
>required for patching. This analysis depends on the instrumentation policy
>chosen by the user. Then, bcov patches the input module by adding two
>program segments; one segment contains trampoline code and the other keeps
>coverage data.
>
>bcov uses trampolines to instrument the binary where it inserts probes in
>targeted locations to track basic block coverage. Each probe consists of a
>detour that diverts control flow to a designated trampoline. The trampoline
>updates coverage data using a single pc-relative mov instruction (e.g., mov
>BYTE PTR [rip+0xadd88],1), executes relocated instructions, and then
>restores control flow to its original state.
>
>To dump coverage data, the user can inject our small runtime library,
>libbcov-rt, using the LD_PRELOAD mechanism. This library scans the memory
>mappings of the current process to dump every data segment that starts with
>a specific magic number. This means that coverage data will be dumped
>separately for each ELF module. Dumping data takes place at process
>shutdown or upon receiving a user signal. The latter feature enables online
>coverage tracking.
>
>
>## Key features
>
>bcov integrates several techniques including:
>
> - Probe pruning. We instrument only what is necessary for tracking code
>coverage. To this end, we bring Agrawal's probe pruning technique [3] to
>binary-level instrumentation.
>
> - Precise CFG analysis. Imprecision in the recovered CFG can have several
>effects, which include causing the instrumented binary to crash. To improve
>CFG precision, we propose a novel jump table analysis and implement a
>variant of an available non-return analysis [4].
>
> - Static instrumentation. We implement several techniques like (1)
>optimized probe selection, (2) jump table instrumentation, (3) and
>systematic detour hosting.
>
>The result is that our approach can work transparently, and with low
>overhead, on large and well-tested C and C++ programs.
>
>
>## Relation to the compiler
>
>Code coverage analysis at the binary level, as implemented in bcov, can
>offer several benefits: first, it is highly flexible. A user can analyze
>the coverage of a particular module only. Even better, coverage tracking
>can be limited to specific functions, e.g., the ones affected by recent
>changes. Second, it is largely independent of the used compiler and the
>build system. We observed consistent results after experimenting with four
>different versions of gcc and clang and three different build types,
>namely, debug, release, and LTO. Finally, the primitives we use to divert
>control-flow and update coverage are simple. One can imagine (and hope)
>that extending bcov to system code, or other native languages, will not
>require a lot of work.
>
>However, the viability of static binary-level coverage analysis ultimately
>depends on the compiler toolchain that produces the binary. Specifically,
>we can think of two areas where compilers might help our tool by adding
>extra support, or at least by not emitting code that is difficult to
>analyze:
>
> - Source-level coverage. The ability to know whether a particular basic
>block was covered in the binary is quite interesting. However, developers
>would ultimately want to map this coverage information to source-level
>artifacts. Statement coverage can be supported already with the help of
>dwarf debugging information. But bcov can report the coverage of individual
>branch decisions at the binary level. Therefore, leveraging this
>information to obtain source-level MC/DC seems to be a natural next step.
>Ideally, we want to infer MC/DC using what we have already, namely,
>binary-level coverage and dwarf information. However, adding support to a
>custom mapping format similar to the one available in llvm-cov might be
>necessary. In this regard, we appreciate your suggestions on the best way
>to support MC/DC, if at all feasible. (feedback needed)
>
> - Function model. We assume that a function occupies a continuous code
>region. The function's main entry is expected to be at the beginning of
>this region. Also, we consider each landing pad to be a function entry as
>well. Based on this, we assume that the identified entries predominate all
>basic blocks inside the function. In other words, a function can not reach
>an arbitrary basic block inside another function without first visiting one
>of its entries. This assumption seems to hold pretty well in the binaries
>we experimented with, but we are not sure to what extent it generalizes to
>other compilers and ISAs. (feedback needed)
>
>
>## Potential future work
>
>Updating coverage using a single pc-relative mov instruction is transparent
>in the sense that it does not clobber any general-purpose register. It even
>preserves the CPU's eflags. This, in turn, has greatly simplified the
>implementation of our prototype. That is, we did not have to think about
>saving/restoring the CPU state. However, putting mechanisms in place to
>allow eflags to be *safely* clobbered, e.g., liveness analysis, can open
>the door for several design alternatives where a pc-relative mov (e.g., mov
>BYTE PTR [rip+0xadd88],1) can be replaced with:
>
>  -  or BYTE PTR [rip+0xadd88],c: where c is one-hot constant. This will
>reduce the size of required coverage data to 1/8 of the size currently
>required by bcov.
>
>  - add BYTE PTR [rip+0xadd88],1: 8-bit counter per superblock (or selected
>set of basic blocks). This might be useful for fuzzing.
>
>  - add DWORD PTR [rip+0xadd88],1: 32-bit counter per superblock (or
>selected set of basic blocks). This can be useful for profiling.
>
>
>So there is significant potential here. However, we have not explored these
>ideas any further, and we would highly appreciate any feedback about their
>viability and potential added value in comparison to what is already
>available in llvm-cov and sancov. (feedback needed)
>
>To conclude, we introduced bcov [1] and discussed a few areas where the
>LLVM community can help us assess its potential, identify related work, and
>plan its future direction. Many thanks in advance for your feedback.
>
>
>Kind regards,
>
>Ammar
>
>
> [1]: https://github.com/abenkhadra/bcov
> [2]: https://arxiv.org/pdf/2004.14191.pdf
> [3]: https://dl.acm.org/doi/10.1145/174675.175935
> [4]: https://dl.acm.org/doi/10.1145/2931037.2931047
Hi Ammar,

Great work!

I think the following statement in the paper is not true: "gcov relies on
debug
information which is less accurate in optimized builds." GCC's gcov
implementation does not rely on debugging information. This can be verified with
-fdump-debug: `gcc --coverage -fdump-debug -c a.c` dumps an empty a.c.*.debug
file.

clang supports gcov instrumentation as well. clang --coverage does leverage
debugging information, but the usage is very minimum - debugging information is
just used to retrieve the start line of a function. I have studied and
contributed a bit to clang's gcov implementation recently and thus I am
fairly
confident about this.
> In comparison, llvm-cov features a custom mapping format embedded in
> LLVM’s intermediate representation (IR).
I don't know whether a clarification is needed here. llvm-cov is a frontend
tool
which supports two formats: (1) coverage mapping (clang -fprofile-instr-generate
-fcoverage-mapping) and (2) gcov. Using -fcoverage-mapping might be clearer in
the context.
> Also, sancov is tightly coupled with LLVM sanitizers (e.g., ASan) which add
> varying overhead. Extending bcov with additional feedback signals, similar
to
> sancov, is an interesting future work
SanitizerCoverage is a standalone instrumentation pass
(llvm/lib/Transforms/Instrumentation/SanitizerCoverage.cpp)
which is not coupled with asan. -fsanitize-coverage= can be used standalone, or 
together with asan, lsan, msan, ubsan, etc.

Its overhead can be very small, especially if you use the recent
inline-bool-flag
https://reviews.llvm.org/D77244
(`clang -fsanitize-coverage=inline-bool-flag a.c`) or inline-8bit-counters.
You can choose 3 modes: edge (default), bb, and func.
The flags are stored in a section __sancov_bools.
There is no standalone dumper though...


A few random comments below.


If I understand correctly, by leveraging superblock dominator graph (from
"Dominators, Super Blocks, and Program Coverage") and the any-node
policy, the
coverage result is binary: not-covered vs covered. I agree that in many cases
this is sufficient. gcov and -fcoverage-mapping, on the other hand, can provide
line execution counts, which are handy in other cases. This fact should probably
mentioned somewhere, "RQ2: Instrumentation overhead" or another place.
Both
-fcoverage-mapping and gcov provide more information, so they have larger
overhead (I agree that gcov isn't a format optimized for file sizes - it
uses
uint32_t records everywhere which can be quite expensive representing smaller
integers).


bcov currently rewrites program headers but not the section header table, so for
example, objdump -d a.any cannot disassemble the synthesized code.


bcov depends on capstone, which appears to be a pretty standard disassembly
tool... It is based on a ~2013 shimmed-down version of LLVM MC and
LLVM*Disassembler. The generated files (e.g. arch/X86/X86DisassemblerDecoder.c)
get ad-hoc sporadic amendment from contributors over the years.. It is a bit
unfortunate for LLVM that MC and LLVM*Disassembler (Machine Code, including
assembly/disassembly libraries) cannot be easily adapted by downstream
users..... (Well, downstream packages can use LLVM exported CMake files and use
find_program(LLVM) and link against some LLVM* libraries but this may pull in a
large set of dependencies)

Hi Fangrui,

Many thanks for providing such detailed feedback! Please find my comments
inlined below.

- Ammar


On Sun, Jun 28, 2020 at 5:59 AM Fangrui Song <maskray at google.com>
wrote:
> On 2020-06-26, Ammar Ben Khadra via llvm-dev wrote:
> >## TL;DR
> >
> >We introduce bcov, an open-source binary-level coverage analysis tool
[1].
> >The details are discussed in our paper [2], which is accepted to
> >ESEC/FSE'20. bcov statically instruments x86-64 ELF binaries
without
> >compiler support. It features several techniques that allow it to
achieve
> >high performance, transparency, and flexibility.
> >
> >For example, running "make check-llvm-codegen" with an
instrumented
> version
> >of llc introduces a mean performance overhead of about 32%. The
overhead
> >for smaller binaries can be as low as 2%. bcov accurately reports code
> >coverage with a mean F-score of 99,86%. All of that without introducing
> any
> >test regressions.
> >
> >
> >## Design overview
> >
> >Given an ELF module as input, bcov will first analyze it to choose
> >appropriate probe locations and estimate the size of the program
segments
> >required for patching. This analysis depends on the instrumentation
policy
> >chosen by the user. Then, bcov patches the input module by adding two
> >program segments; one segment contains trampoline code and the other
keeps
> >coverage data.
> >
> >bcov uses trampolines to instrument the binary where it inserts probes
in
> >targeted locations to track basic block coverage. Each probe consists
of a
> >detour that diverts control flow to a designated trampoline. The
> trampoline
> >updates coverage data using a single pc-relative mov instruction (e.g.,
> mov
> >BYTE PTR [rip+0xadd88],1), executes relocated instructions, and then
> >restores control flow to its original state.
> >
> >To dump coverage data, the user can inject our small runtime library,
> >libbcov-rt, using the LD_PRELOAD mechanism. This library scans the
memory
> >mappings of the current process to dump every data segment that starts
> with
> >a specific magic number. This means that coverage data will be dumped
> >separately for each ELF module. Dumping data takes place at process
> >shutdown or upon receiving a user signal. The latter feature enables
> online
> >coverage tracking.
> >
> >
> >## Key features
> >
> >bcov integrates several techniques including:
> >
> > - Probe pruning. We instrument only what is necessary for tracking
code
> >coverage. To this end, we bring Agrawal's probe pruning technique
[3] to
> >binary-level instrumentation.
> >
> > - Precise CFG analysis. Imprecision in the recovered CFG can have
several
> >effects, which include causing the instrumented binary to crash. To
> improve
> >CFG precision, we propose a novel jump table analysis and implement a
> >variant of an available non-return analysis [4].
> >
> > - Static instrumentation. We implement several techniques like (1)
> >optimized probe selection, (2) jump table instrumentation, (3) and
> >systematic detour hosting.
> >
> >The result is that our approach can work transparently, and with low
> >overhead, on large and well-tested C and C++ programs.
> >
> >
> >## Relation to the compiler
> >
> >Code coverage analysis at the binary level, as implemented in bcov, can
> >offer several benefits: first, it is highly flexible. A user can
analyze
> >the coverage of a particular module only. Even better, coverage
tracking
> >can be limited to specific functions, e.g., the ones affected by recent
> >changes. Second, it is largely independent of the used compiler and the
> >build system. We observed consistent results after experimenting with
four
> >different versions of gcc and clang and three different build types,
> >namely, debug, release, and LTO. Finally, the primitives we use to
divert
> >control-flow and update coverage are simple. One can imagine (and hope)
> >that extending bcov to system code, or other native languages, will not
> >require a lot of work.
> >
> >However, the viability of static binary-level coverage analysis
ultimately
> >depends on the compiler toolchain that produces the binary.
Specifically,
> >we can think of two areas where compilers might help our tool by adding
> >extra support, or at least by not emitting code that is difficult to
> >analyze:
> >
> > - Source-level coverage. The ability to know whether a particular
basic
> >block was covered in the binary is quite interesting. However,
developers
> >would ultimately want to map this coverage information to source-level
> >artifacts. Statement coverage can be supported already with the help of
> >dwarf debugging information. But bcov can report the coverage of
> individual
> >branch decisions at the binary level. Therefore, leveraging this
> >information to obtain source-level MC/DC seems to be a natural next
step.
> >Ideally, we want to infer MC/DC using what we have already, namely,
> >binary-level coverage and dwarf information. However, adding support to
a
> >custom mapping format similar to the one available in llvm-cov might be
> >necessary. In this regard, we appreciate your suggestions on the best
way
> >to support MC/DC, if at all feasible. (feedback needed)
> >
> > - Function model. We assume that a function occupies a continuous code
> >region. The function's main entry is expected to be at the
beginning of
> >this region. Also, we consider each landing pad to be a function entry
as
> >well. Based on this, we assume that the identified entries predominate
all
> >basic blocks inside the function. In other words, a function can not
reach
> >an arbitrary basic block inside another function without first visiting
> one
> >of its entries. This assumption seems to hold pretty well in the
binaries
> >we experimented with, but we are not sure to what extent it generalizes
to
> >other compilers and ISAs. (feedback needed)
> >
> >
> >## Potential future work
> >
> >Updating coverage using a single pc-relative mov instruction is
> transparent
> >in the sense that it does not clobber any general-purpose register. It
> even
> >preserves the CPU's eflags. This, in turn, has greatly simplified
the
> >implementation of our prototype. That is, we did not have to think
about
> >saving/restoring the CPU state. However, putting mechanisms in place to
> >allow eflags to be *safely* clobbered, e.g., liveness analysis, can
open
> >the door for several design alternatives where a pc-relative mov (e.g.,
> mov
> >BYTE PTR [rip+0xadd88],1) can be replaced with:
> >
> >  -  or BYTE PTR [rip+0xadd88],c: where c is one-hot constant. This
will
> >reduce the size of required coverage data to 1/8 of the size currently
> >required by bcov.
> >
> >  - add BYTE PTR [rip+0xadd88],1: 8-bit counter per superblock (or
> selected
> >set of basic blocks). This might be useful for fuzzing.
> >
> >  - add DWORD PTR [rip+0xadd88],1: 32-bit counter per superblock (or
> >selected set of basic blocks). This can be useful for profiling.
> >
> >
> >So there is significant potential here. However, we have not explored
> these
> >ideas any further, and we would highly appreciate any feedback about
their
> >viability and potential added value in comparison to what is already
> >available in llvm-cov and sancov. (feedback needed)
> >
> >To conclude, we introduced bcov [1] and discussed a few areas where the
> >LLVM community can help us assess its potential, identify related work,
> and
> >plan its future direction. Many thanks in advance for your feedback.
> >
> >
> >Kind regards,
> >
> >Ammar
> >
> >
> > [1]: https://github.com/abenkhadra/bcov
> > [2]: https://arxiv.org/pdf/2004.14191.pdf
> > [3]: https://dl.acm.org/doi/10.1145/174675.175935
> > [4]: https://dl.acm.org/doi/10.1145/2931037.2931047
>
> Hi Ammar,
>
> Great work!
>
> I think the following statement in the paper is not true: "gcov relies
on
> debug
> information which is less accurate in optimized builds." GCC's
gcov
> implementation does not rely on debugging information. This can be
> verified with
> -fdump-debug: `gcc --coverage -fdump-debug -c a.c` dumps an empty
> a.c.*.debug
> file.
>
>
After a second look at
https://gcc.gnu.org/onlinedocs/gcc/Gcov-Intro.html#Gcov-Intro, it seems
that I misunderstood why it is recommended to disable optimizations in
gcov. You are right. This should be fixed. Thanks!

> clang supports gcov instrumentation as well. clang --coverage does leverage
> debugging information, but the usage is very minimum - debugging
> information is
> just used to retrieve the start line of a function. I have studied and
> contributed a bit to clang's gcov implementation recently and thus I am
> fairly
> confident about this.
>
> > In comparison, llvm-cov features a custom mapping format embedded in
> > LLVM’s intermediate representation (IR).
>
> I don't know whether a clarification is needed here. llvm-cov is a
> frontend tool
> which supports two formats: (1) coverage mapping (clang
> -fprofile-instr-generate
> -fcoverage-mapping) and (2) gcov. Using -fcoverage-mapping might be
> clearer in
> the context.
>
>
Actually, we are referring to the first usage, which is, as I understand,
the most popular. A clarification can indeed help here.

> > Also, sancov is tightly coupled with LLVM sanitizers (e.g., ASan)
which
> add
> > varying overhead. Extending bcov with additional feedback signals,
> similar to
> > sancov, is an interesting future work
>
> SanitizerCoverage is a standalone instrumentation pass
> (llvm/lib/Transforms/Instrumentation/SanitizerCoverage.cpp)
> which is not coupled with asan. -fsanitize-coverage= can be used
> standalone, or
> together with asan, lsan, msan, ubsan, etc.
>
> Its overhead can be very small, especially if you use the recent
> inline-bool-flag
> https://reviews.llvm.org/D77244
> (`clang -fsanitize-coverage=inline-bool-flag a.c`) or inline-8bit-counters.
> You can choose 3 modes: edge (default), bb, and func.
> The flags are stored in a section __sancov_bools.
> There is no standalone dumper though...
>
>
>
I remember that the term "tightly coupled" was used by a sancov
developer
in a Github issue. I can not find that thread now. This perhaps refers to
the fact that (please correct me) casual users are not expected to use
sancov as an independent pass. Instead, they should rely on the runtime
already provided by one of the sanitizers. I shall revisit this issue to
improve the wording. However, I think that the main point still holds;
given this dependence on the sanitizer runtime and the variety of options
offered by sancov, one can not refer to a 'reference' sancov to report
its instrumentation overhead. Nevertheless, the functionality offered by
the sancov option 'trace-pc-guard' is perhaps closest to that in bcov.
If
we use this as the basis for measurement, then the performance overhead
should be small. Can you please refer me to instructions on how I can use
sancov without any sanitizer runtime?


A few random comments below.
>
>
> If I understand correctly, by leveraging superblock dominator graph (from
> "Dominators, Super Blocks, and Program Coverage") and the
any-node policy,
> the
> coverage result is binary: not-covered vs covered. I agree that in many
> cases
> this is sufficient. gcov and -fcoverage-mapping, on the other hand, can
> provide
> line execution counts, which are handy in other cases. This fact should
> probably
> mentioned somewhere, "RQ2: Instrumentation overhead" or another
place.
> Both
> -fcoverage-mapping and gcov provide more information, so they have larger
> overhead (I agree that gcov isn't a format optimized for file sizes -
it
> uses
> uint32_t records everywhere which can be quite expensive representing
> smaller
> integers).
>
>
>
Yes, the reported coverage is binary. It indicates whether a basic block is
covered or not based on the coverage of its superblock (a set of basic
blocks with the same coverage information). Profiling represents an
interesting use case, indeed. I noted in the 'Potential future work'
section that updating coverage data using:

add DWORD PTR [rip+0xadd88],1

instead of the current:

mov BYTE PTR [rip+0xadd88],1

Enables profiling using a 32-bit counter per superblock. However, a
superblock can span multiple lines in the source code which renders this
counter difficult to relate to. Instead of grouping basic blocks based on
superblocks, we need to think about mapping each source line to its
corresponding basic blocks in the binary and then choose the best BB to
instrument with such a counter. Expectedly, this will incur more overhead.

Also, note that "RQ2: Instrumentation overhead" is concerned with
evaluating the overhead of bcov only. We did not attempt to directly
compare this overhead to that of llvm-cov or gcov since, as you noted
already, both tools track different artifacts, i.e., not directly
comparable. However, we mentioned that llvm-cov can dump "320GB of coverage
(and profiling) data" if online merging was not enabled. So there was an
explicit emphasis on the role of profiling here. And that discussion was in
the context of showing that the same online merging feature can be
leveraged by bcov as well.


> bcov currently rewrites program headers but not the section header table,
> so for
> example, objdump -d a.any cannot disassemble the synthesized code.
>
>
>
The original section header table is preserved. But adding two additional
section headers to describe the patch segments is a nice to have feature. I
developed a simple tool to disassemble the patch code which was sufficient
for my debugging needs. I'm planning to open source this tool later. In the
meantime, using gdb as described in the artifacts repository (
https://github.com/abenkhadra/bcov-artifacts/blob/master/sample-binaries/README.md)
might be the easiest way to inspect the patch code.


> bcov depends on capstone, which appears to be a pretty standard disassembly
> tool... It is based on a ~2013 shimmed-down version of LLVM MC and
> LLVM*Disassembler. The generated files (e.g.
> arch/X86/X86DisassemblerDecoder.c)
> get ad-hoc sporadic amendment from contributors over the years.. It is a
> bit
> unfortunate for LLVM that MC and LLVM*Disassembler (Machine Code, including
> assembly/disassembly libraries) cannot be easily adapted by downstream
> users..... (Well, downstream packages can use LLVM exported CMake files
> and use
> find_program(LLVM) and link against some LLVM* libraries but this may pull
> in a
> large set of dependencies)
>
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