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ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures
ControlFlag is a self-supervised idiosyncratic pattern detection system that learns typical patterns that occur in the control structures of high-level programming languages, such as C/C++, by mining these patterns from open-source repositories (on GitHub and other version control systems). It then applies learned patterns to detect anomalous patterns in user's code.
Brief technical description
ControlFlag's pattern anomaly detection system can be used for various problems such as typographical error detection, flagging a missing NULL check to name a few. This PoC demonstrates ControlFlag's application in the typographical error detection.
Figure below shows ControlFlag's two main phases: (1) pattern mining phase, and (2) scanning for anomalous patterns phase. The pattern mining phase is a "training phase" that mines typical patterns in the user-provided GitHub repositories and then builds a decision-tree from the mined patterns. The scanning phase, on the other hand, applies the mined patterns to flag anomalous expressions in the user-specified target repositories.
More details can be found in our MAPS paper (https://arxiv.org/abs/2011.03616).
Directory structure (evolving)
src: Source code for ControlFlag for typographical error detection system
scripts: Scripts for pattern mining and scanning for anomalies
quick_start: Scripts to run quick start tests
github: Scripts and data for downloading GitHub repos. It also contains pre-processed training data containing patterns mined from 6000 GitHub repositories using C as their primary language.
tests: unit tests
ControlFlag can be built on Linux and MacOS.
- CMake 3.4.3 or above
- C++17 compatible compiler
- Tree-sitter parser (downloaded automatically as part of cmake)
- GNU parallel (optional, if you want to generate your own training data)
Tested build configuration on Linux-based systems
- CentOS-7.6/Ubuntu-20.04 with g++-v10.2.0 for x86_64
Tested build configuration on MacOS
- MacOS Mojave v10.14.6 with clang-1001.0.46.4 (Apple LLVM version 10.0.1) for x86_64 (obtained from The Command Line Tools Package)
$ cd control-flag $ cmake . $ make -j $ make test
All tests in
make test should pass, but currently tests for Verilog are failing because of a version mismatch issue. Verilog support is WIP.
Using patterns obtained from 6000 GitHub repos to scan repository of your choice
Download the training data for C language depending on the memory constraints of your device. Note, however, that using smaller datasets may lead to reduced accuracy in the results ControlFlag produces and possibly an increase in the number of false positives it generates.
|Dataset name||Size on disk||Memory requirements||Direct link||gdown ID||MD5 checksum|
$ python -m pip install gdown && gdown https://drive.google.com/uc?id=<id_from_table> $ (optional) md5sum <tgz_file> $ tar -zxf <tgz_file>
To scan C code of your choice, use below command:
$ scripts/scan_for_anomalies.sh -d <directory_to_be_scanned_for_anomalies> -t <training_data>.ts -o <output_directory_to_store_log_files>
Once the run is complete (which could take some time depending on your system and the number of C programs in your repository,) refer to the section below to understand scan output.
Mining patterns from a small repo and applying them to another small repo
Simply run below command:
cd quick_start && ./test1_c.sh
If everything goes well, you can see output from the scanner in
test1_scan_output directory. Look for "Potential anomaly" label in it by
grep "Potential anomaly" -C 5 \*.log, and you should see output like below:
thread_6.log-Level:TWO Expression:(parenthesized_expression (binary_expression ("==") (identifier) (non_terminal_expression))) found in training dataset: Source file: brubeck/src/server.c:266:5:(s == sizeof(fdsi)) thread_6.log-Autocorrect search took 0.000 secs thread_6.log:Potential anomaly thread_6.log-Did you mean:(parenthesized_expression (binary_expression ("==") (identifier) (non_terminal_expression))) with editing cost:0 and occurrences: 1 thread_6.log-Did you mean:(parenthesized_expression (binary_expression ("==") (identifier) (null))) with editing cost:1 and occurrences: 25 thread_6.log-Did you mean:(parenthesized_expression (binary_expression ("==") (identifier) (identifier))) with editing cost:1 and occurrences: 5 thread_6.log-Did you mean:(parenthesized_expression (binary_expression (">=") (identifier) (non_terminal_expression))) with editing cost:1 and occurrences: 3 thread_6.log-Did you mean:(parenthesized_expression (binary_expression ("==") (non_terminal_expression) (non_terminal_expression))) with editing cost:1 and occurrences: 2
The anomaly is flagged for
brubeck/src/server.c at line number
- Pattern Mining phase (if you want to generate training data yourself)
If you do not want to generate training data yourself, go to Evaluation step below.
In this phase, we mine the idiosyncratic patterns that appear in the control structures of high-level language such as C. This PoC mines patterns from
if statements that appear in C programs.
If you want to use your own repository for mining patterns, jump to Step 1.2.
1.1 Downloading Top-100 GitHub repos for C language
Steps below show how to download Top-100 GitHub repos for C language (
c100.txt) and generate training data.
training_repo_dir is a directory where the command below will clone all the repos.
$ cd github $ python download_repos.py -f c100.txt -o <training_repo_dir> -m clone -p 5
1.2 Mining patterns from downloaded repositories
You can use your own repository to mine for expressions by passing it in place of <training_repo_dir>.
mine_patterns.sh script helps for this. It's usage is as below:
Usage: ./mine_patterns.sh -d <directory_to_mine_patterns_from> -o <output_file_to_store_training_data> Optional: [-n number_of_processes_to_use_for_mining] (default: num_cpus_on_system) [-l source_language_number] (default: 1 (C), supported: 1 (C), 2 (Verilog)
We use it as:
$ scripts/mine_patterns.sh -d <training_repo_dir> -o <training_data_file> -l 1
<training_dat_file> contains conditional expressions in C language that are found in the specified GitHub repos and their AST (abstract syntax tree) representations. You can view this file as a text file, if you want.
Evaluation (or scanning for anomalies in C code from test repo)
We can run
scan_for_anomalies.sh script to scan target directory of interest. Its usage is as below.
Usage: ./scan_for_anomalies.sh -t <training_data> -d <directory_to_scan_for_anomalous_patterns> Optional: [-c max_cost_for_autocorrect] (default: 2) [-n max_number_of_results_for_autocorrect] (default: 5) [-j number_of_scanning_threads] (default: num_cpus_on_systems) [-o output_log_dir] (default: /tmp) [-l source_language_number] (default: 1 (C), supported: 1 (C), 2 (Verilog)) [-a anomaly_threshold] (default: 3.0)
scripts/scan_for_anomalies.sh -d <test_directory> -t <training_data_file> -o <output_log_dir>
As a part of scanning for anomalies, ControlFlag also suggests possible corrections in case a conditional expression is flagged as an anomaly.
25 is the
max_cost for the correction -- how close should the suggested correction be to possibly mistyped expression. Increasing
max_cost leads to suggesting more corrections. If you feel that the number of reported anomalies is high, consider reducing
1.0 or less.
Understanding scan output
output_log_dir you will find multiple log files corresponding to the scan output from different scanner threads. Potential anomalies are reported with "Potential anomaly" as a label. Command below will report log files containing at least one anomaly.
$ grep "Potential anomaly" <output_log_dir>/thread_*.log
A sample anomaly report looks like below:
Level:<ONE or TWO> Expression: <AST_for_anomalous_expression> Source file and line number: <C code with line number having the anomaly> Potential anomaly Did you mean ...
The text after "Did you mean" shows possible corrections to the anomalous expression.