Stack Analysis

List of models currently present in the analytics platform

• Gnosis
• Pgm
• Softnet
• Apollo
• Uranus

To Deploy Locally

Set up .env file with environment variables, i.e (view docker-compose.yml for possible values)

cat > .env <<-EOF
# Amazon AWS S3 credentials
AWS_S3_ACCESS_KEY_ID=
AWS_S3_SECRET_ACCESS_KEY=

# Kronos environment
KRONOS_SCORING_REGION=
DEPLOYMENT_PREFIX=
GREMLIN_REST_URL=

#Set Post Filtering
USE_FILTERS=
EOF

NOTES:\ Do not use any [#] comments or ['"] in the .env file.\ For the GREMLIN_REST_URL, you can take a look at out data-model and use the local-setup services

git clone https://github.com/fabric8-analytics/fabric8-analytics-data-model.git
cp -r fabric8-analytics-data-model/local-setup/scripts .
cp fabric8-analytics-data-model/local-setup/docker-compose.yml docker-compose-data-model.yml

# and in .env file
GREMLIN_REST_URL="http://localhost:8182"  # Note that the port is a port accessed from within the container

Otherwise you can use custom gremlin service

Deploy with docker-compose:\

docker-compose build
docker-compose -f docker-compose.yml -f docker-compose-data-model.yml up

To Test Locally

python -m unittest discover tests -v

To Run Evaluation Script Locally

PYTHONPATH=`pwd` python evaluation_platform/uranus/src/kronos_offline_evaluation.py

To Run Training Locally

PYTHONPATH=`pwd` python analytics_platform/kronos/src/kronos_offline_training.py

Deploy to openshift cluster

• Create project

oc new-project fabric8-analytics-stack-analysis

• Deploy secrets and config map

oc apply -f secret.yaml
oc apply -f config.yaml

• Deploy app using oc

oc process -f openshift/template.yaml | oc apply -f -

Sample Evaluation Request Input

Request Type: POST
ENDPOINT: api/v1/schemas/kronos_evaluation
BODY: JSON data
{
    "training_data_url":"s3://dev-stack-analysis-clean-data/maven/github/"
}

Sample Scoring Request Input

Request Type: POST 
ENDPOINT: /api/v1/schemas/kronos_scoring
BODY: JSON data
[
        {
            "ecosystem": "maven",
            "comp_package_count_threshold": 5,
            "alt_package_count_threshold": 2,
            "outlier_probability_threshold": 0.88,
            "unknown_packages_ratio_threshold": 0.3,
            "package_list": [         
            "io.vertx:vertx-core",
            "io.vertx:vertx-web"
    ]
        }
]

Sample Response

[
    {
        "alternate_packages": {
            "io.vertx:vertx-core": [
                {
                    "package_name": "io.netty:netty-codec-http",
                    "similarity_score": 1,
                    "topic_list": [
                        "http",
                        "network",
                        "netty",
                        "socket"
                    ]
                }
            ],
            "io.vertx:vertx-web": [
                {
                    "package_name": "org.jspare:jspare-core",
                    "similarity_score": 1,
                    "topic_list": [
                        "framework",
                        "webapp"
                    ]
                }
            ]
        },
        "companion_packages": [
            {
                "cooccurrence_count": 219,
                "cooccurrence_probability": 83.26996197718631,
                "package_name": "org.slf4j:slf4j-api",
                "topic_list": [
                    "logging",
                    "dependency-injection",
                    "api"
                ]
            },
            {
                "cooccurrence_count": 205,
                "cooccurrence_probability": 77.9467680608365,
                "package_name": "org.apache.logging.log4j:log4j-core",
                "topic_list": [
                    "logging",
                    "java"
                ]
            },
            {
                "cooccurrence_count": 208,
                "cooccurrence_probability": 79.08745247148289,
                "package_name": "io.vertx:vertx-web-client",
                "topic_list": [
                    "http",
                    "http-request",
                    "vertx-web-client",
                    "http-response"
                ]
            }
        ],
        "ecosystem": "maven",
        "missing_packages": [],
        "outlier_package_list": [
            {
                "frequency_count": 100,
                "package_name": "io.vertx:vertx-core",
                "topic_list": [
                    "http",
                    "socket",
                    "tcp",
                    "reactive"
                ]
            },
            {
                "frequency_count": 90,
                "package_name": "io.vertx:vertx-web",
                "topic_list": [
                    "vertx-web",
                    "webapp",
                    "auth",
                    "routing"
                ]
            }
        ],
        "package_to_topic_dict": {
            "io.vertx:vertx-core": [
                "http",
                "socket",
                "tcp",
                "reactive"
            ],
            "io.vertx:vertx-web": [
                "vertx-web",
                "webapp",
                "auth",
                "routing"
            ]
        },
        "user_persona": "1"
    }
]

Latest Deployment

• Maven
  • Retrained on: 2018-04-11 5:43 PM(IST) with hyper-parameters:
    • fp_min_support_count = 300
    • fp_intent_topic_count_threshold = 2
    • FP_TAG_INTENT_LIMIT = 4
  • Used pomegranate version: 0.7.3

Footnotes

Check for all possible issues

The script named check-all.sh is to be used to check the sources for all detectable errors and issues. This script can be run w/o any arguments:

./check-all.sh

Expected script output:

Running all tests and checkers
  Check all BASH scripts
    OK
  Check documentation strings in all Python source file
    OK
  Detect common errors in all Python source file
    OK
  Detect dead code in all Python source file
    OK
  Run Python linter for Python source file
    OK
  Unit tests for this project
    OK
Done

Overall result
  OK

An example of script output when one error is detected:

Running all tests and checkers
  Check all BASH scripts
    Error: please look into files check-bashscripts.log and check-bashscripts.err for possible causes
  Check documentation strings in all Python source file
    OK
  Detect common errors in all Python source file
    OK
  Detect dead code in all Python source file
    OK
  Run Python linter for Python source file
    OK
  Unit tests for this project
    OK
Done

Overal result
  One error detected!

Please note that the script creates bunch of *.log and *.err files that are temporary and won't be commited into the project repository.

Coding standards

• You can use scripts run-linter.sh and check-docstyle.sh to check if the code follows PEP 8 and PEP 257 coding standards. These scripts can be run w/o any arguments:

./run-linter.sh
./check-docstyle.sh

The first script checks the indentation, line lengths, variable names, white space around operators etc. The second script checks all documentation strings - its presence and format. Please fix any warnings and errors reported by these scripts.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Code complexity measurement

The scripts measure-cyclomatic-complexity.sh and measure-maintainability-index.sh are used to measure code complexity. These scripts can be run w/o any arguments:

./measure-cyclomatic-complexity.sh
./measure-maintainability-index.sh

The first script measures cyclomatic complexity of all Python sources found in the repository. Please see this table for further explanation how to comprehend the results.

The second script measures maintainability index of all Python sources found in the repository. Please see the following link with explanation of this measurement.

You can specify command line option --fail-on-error if you need to check and use the exit code in your workflow. In this case the script returns 0 when no failures has been found and non zero value instead.

Dead code detection

The script detect-dead-code.sh can be used to detect dead code in the repository. This script can be run w/o any arguments:

./detect-dead-code.sh

Please note that due to Python's dynamic nature, static code analyzers are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused.

Because of this potential problems, only code detected with more than 90% of confidence is reported.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Common issues detection

The script detect-common-errors.sh can be used to detect common errors in the repository. This script can be run w/o any arguments:

./detect-common-errors.sh

Please note that only semantical problems are reported.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Check for scripts written in BASH

The script named check-bashscripts.sh can be used to check all BASH scripts (in fact: all files with the .sh extension) for various possible issues, incompatibilities, and caveats. This script can be run w/o any arguments:

./check-bashscripts.sh

Please see the following link for further explanation, how the ShellCheck works and which issues can be detected.

Code coverage report

Code coverage is reported via the codecov.io. The results can be seen on the following address:

code coverage report