Learn how Aibolit works in our White Paper.
First, you install it (you must have Python 3.7.7 and Pip installed):
pip3 install aibolit==1.3.0To analyze your Java sources, located at src/java (for example), run:
aibolit check --filenames src/java/File.java src/java/AnotherFile.javaor
aibolit recommend --filenames src/java/File.java src/java/AnotherFile.javaAlso, you can set a folder with Java files:
aibolit recommend --folder src/javaIt will run recommendation function for the model (model is located in
aibolit/binary_files/model.pkl).
The model finds a pattern which contribution is the largest to the
Cyclomatic Complexity.
If anything is found, you will see all recommendations for the mentioned
patterns.
You can see the list of all patterns in
Patterns.md.
The output of recommendation will be redirected to the stdout.
If the program has the 0 exit code, it means that all analyzed files do
not have any issues.
If the program has the 1 exit code, it means that at least 1 analyzed file
has an issue.
If the program has the 2 exit code, it means that program crash occurred.
You can suppress certain patterns (comma separated value) and they will be ignored. They won't be included into the report, also their importance will be set to 0.
aibolit recommend --folder src/java --suppress=P12,P13You can change the format, using the --format parameter. The default value
is --format=compact.
aibolit recommend --folder src/java --format=compact --fullIt will output sorted patterns by importance in descending order and grouped by a pattern name:
Show all patterns
Configuration.java score: 127.67642529949538
Configuration.java[3840]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[3844]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[3848]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[2411]: Null Assignment (P28: 10.76 2/4)
Configuration.java[826]: Many primary constructors (P9: 10.76 3/4)
Configuration.java[840]: Many primary constructors (P9: 10.76 3/4)
Configuration.java[829]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[841]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[865]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[2586]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3230]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3261]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3727]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3956]: Partial synchronized (P14: 0.228 4/4)
ErrorExample.java: error when calculating patterns: Can't count P1 metric:
Total score: 127.67642529949538
(P21: 30.95612931128819 1/4) means the following:
30.95612931128819 is the score of this pattern
1 is the position of this pattern in the total list of patterns
found in the file 4 is the total number of found patterns
You can use format=long. In this case all results will be sorted by a
line number:
Show all patterns
Configuration.java: some issues found
Configuration.java score: 127.67642529949538
Configuration.java[826]: Many primary constructors (P9: 10.76 3/4)
Configuration.java[829]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[840]: Many primary constructors (P9: 10.76 3/4)
Configuration.java[841]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[865]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[2411]: Null Assignment (P28: 10.76 2/4)
Configuration.java[2586]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3230]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3261]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3727]: Partial synchronized (P14: 0.228 4/4)
Configuration.java[3840]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[3844]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[3848]: Var in the middle (P21: 30.95612931128819 1/4)
Configuration.java[3956]: Partial synchronized (P14: 0.228 4/4)
ErrorExample.java: error when calculating patterns: Can't count P1 metric:
MavenSlice.java: your code is perfect in aibolit's opinion
Total score: 127.67642529949538
You can also choose xml format. It will have the same format as compact
mode, but xml will be created:
<report>
<score>127.67642529949538</score>
<!--Show all patterns-->
<files>
<file>
<path>Configuration.java</path>
<summary>Some issues found</summary>
<score>127.67642529949538</score>
<patterns>
<pattern code="P13">
<details>Null check</details>
<lines>
<number>294</number>
<number>391</number>
</lines>
<score>30.95612931128819</score>
<order>1/4</order>
</pattern>
<pattern code="P12">
<details>Non final attribute</details>
<lines>
<number>235</number>
</lines>
<score>10.76</score>
<order>2/4</order>
</pattern>
<pattern code="P21">
<details>Var in the middle</details>
<lines>
<number>235</number>
</lines>
<score>2.056</score>
<order>3/4</order>
</pattern>
<pattern code="P28">
<details>Null Assignment</details>
<lines>
<number>2411</number>
</lines>
<score>0.228</score>
<order>4/4</order>
</pattern>
</patterns>
</file>
<file>
<path>ErrorExample.java</path>
<summary>Error when calculating patterns: Can't count P1 metric:</summary>
</file>
<file>
<path>MavenSlice.java</path>
<summary>Your code is perfect in aibolit's opinion</summary>
</file>
</files>
</report>The score is the relative importance of the pattern (there is no range for it). The larger score is, the most important pattern is. E.g., if you have several patterns, first you need to fix the pattern with the score 5.45:
SampleTests.java[43]: Non final attribute (P12: 5.45 1/10)
SampleTests.java[44]: Non final attribute (P12: 5.45 1/10)
SampleTests.java[80]: Var in the middle (P21: 3.71 2/10)
SampleTests.java[121]: Var in the middle (P21: 3.71 2/10)
SampleTests.java[122]: Var declaration distance for 5 lines (P20_5: 2.13 3/10)
SampleTests.java[41]: Non final class (P24: 1.95 4/10)
SampleTests.java[59]: Force Type Casting (P5: 1.45 5/10)
SampleTests.java[122]: Var declaration distance for 7 lines (P20_7: 1.07 6/10)
SampleTests.java[122]: Var declaration distance for 11 lines (P20_11: 0.78 7/10)
SampleTests.java[51]: Protected Method (P30: 0.60 8/10)
SampleTests.java[52]: Super Method (P18: 0.35 9/10)
SampleTests.java[100]: Partial synchronized (P14: 0.08 10/10)
SampleTests.java[106]: Partial synchronized (P14: 0.08 10/10)
SampleTests.java[113]: Partial synchronized (P14: 0.08 10/10)
The score per class is the sum of all patterns scores.
SampleTests.java score: 17.54698560768407
The total score is an average among all java files in a project (folder you've set to analyze)
Total average score: 4.0801854775508914
If you have 2 scores of different projects, the worst project is that one which has the highest score.
Model is automatically installed with aibolit package, but you can also try your own model
aibolit recommend --folder src/java --model /mnt/d/some_folder/model.pklYou can get full report with --full command, then all patterns will be
included to the output:
aibolit recommend --folder src/java --fullYou can exclude files with --exclude command.
You to set glob patterns to ignore:
aibolit recommend --folder src/java \
--exclude=**/*Test*.java --exclude=**/*Impl*.javaIf you need help, run
aibolit recommend --helpTrain command does the following:
- Calculates patterns and metrics
- Creates a dataset
- Trains model and save it
Train works only with cloned git repository.
- Clone aibolit repository
- Go to
cloned_aibolit_path - Run
pip install . - Set env variable
export HOME_AIBOLIT=cloned_aibolit_path(example for Linux). - Set env variable
TARGET_FOLDERif you need to save all dataset files to another directory. - You have to specify train and test dataset: set the
HOME_TRAIN_DATASETenvironment variable for train dataset and theHOME_TEST_DATASETenvironment variable for test dataset.
Usually, these files are in scripts/target/08 directory after dataset
collection (if you have not skipped it).
But you can use your own datasets.
Please notice, that if you set TARGET_FOLDER, your dataset files will be
in TARGET_FOLDER/target.
That is why it is necessary to
set HOME_TRAIN_DATASET=TARGET_FOLDER\target\08\08-train.csv,
HOME_TEST_DATASET =TARGET_FOLDER\target\08\08-test.csv
7. If you need to set up own directory where model will be saved, set up also
SAVE_MODEL_FOLDER environment variable.
Otherwise model will be saved into
cloned_aibolit_path/aibolit/binary_files/model.pkl
8. If you need to set up own folder with Java files, use --java_folder parameter, the default value will be scripts/target/01 of aibolit cloned
repo
Or you can use our docker image (link will be soon here)
Run train pipeline:
aibolit train --java_folder=src/java [--max_classes=100] [--dataset_file]If you need to save the dataset with all calculated metrics to a different
directory, you need to use dataset_file parameter
aibolit train --java_folder=src/java --dataset_file /mnt/d/new_dir/dataset.csvYou can skip dataset collection with skip_collect_dataset parameter. In
this case
the model will be trained with predefined dataset (see 5 point):
aibolit train --java_folder=src/java --skip_collect_datasetFirst, you need to install:
Install the following packages if you don't have:
apt-get install ruby-dev libz-dev libxml2This project does not include a virtual environment by default. If you're using one (e.g., .venv, venv), update the .xcop file to exclude it:
--exclude=.venv/**After forking and editing the repo, verify the build is clean by running:
makeTo build white paper:
cd wp
latexmk -c && latexmk -pdf wp.texIf everything is fine, submit a pull request.
Using Docker recommendation pipeline
docker run --rm -it \
-v <absolute_path_to_folder_with_classes>:/in \
-v <absolute_path_to_out_dir>:/out \
cqfn/aibolit-image