Auto-analysis service for ReportPortal

Environment variables for configuration

Property name	Type	Default value	Description
ES_HOSTS	string	http://elasticsearch:9200	Elasticsearch host (can be either like this "http://elasticsearch:9200", or with login and password delimited by : and separated from the host name by @)
ES_USER	string		Elasticsearch host login, set up here the username for elasticsearch, if you choose setup username here, in the ES_HOSTS you should leave only url without login and password
ES_PASSWORD	string		Elasticsearch host password, set up here the password for elasticsearch, if you choose setup the password here, in the ES_HOSTS you should leave only url without login and password
LOGGING_LEVEL	string	DEBUG	logging level for the whole module, can be DEBUG, INFO, ERROR, CRITICAL
AMQP_URL	string		an url to the rabbitmq instance
AMQP_EXCHANGE_NAME	string	analyzer	Exchange name for the module communication for this module
ANALYZER_PRIORITY	integer	1	priority for this analyzer
ANALYZER_INDEX	boolean	true	the parameter for rabbitmq exchange params, where the analyzer supports indexing
ANALYZER_LOG_SEARCH	boolean	true	the parameter for rabbitmq exchange params, where the analyzer supports searching logs
ANALYZER_SUGGEST	boolean	true	the parameter for rabbitmq exchange params, where the analyzer supports suggesting
ANALYZER_CLUSTER	boolean	true	the parameter for rabbitmq exchange params, where the analyzer supports clustering
ES_VERIFY_CERTS	boolean	false	turn on SSL certificates verification
ES_USE_SSL	boolean	false	turn on SSL
ES_SSL_SHOW_WARN	boolean	false	show warning on SSL certificates verification
ES_CA_CERT	string		provide a path to CA certs on disk
ES_CLIENT_CERT	string		PEM formatted SSL client certificate
ES_CLIENT_KEY	string		EM formatted SSL client key
ES_TURN_OFF_SSL_VERIFICATION	boolean	false	Turn off ssl verification via using RequestsHttpConnection class instead of Urllib3HttpConnection class.
ANALYZER_BINSTORE_TYPE	enum	filesystem	Possible values: "minio", "filesystem". Strategy where to store information, connected with the analyzer
MINIO_SHORT_HOST	string	minio:9000	you need to set short host and port to the minio service. This property is used in case ANALYZER_BINARYSTORE_TYPE is set to minio.
MINIO_ACCESS_KEY	string	minio	you need to set a minio access key here
MINIO_SECRET_KEY	string	minio123	you need to set a minio secret key here
MINIO_USE_TLS	boolean	false	Flag to indicate to use secure (TLS) connection to S3 service or not.
ANALYZER_BINSTORE_BUCKETPREFIX	string	prj-	the prefix for buckets which are added to each project filepath.
ANALYZER_BINSTORE_MINIO_REGION	string		the region which you can specify for saving in AWS S3
INSTANCE_TASK_TYPE	string		if you want to run a standard analyzer instance, leave it as blank. If you want to run an instance for training, set "train" here.
FILESYSTEM_DEFAULT_PATH	string	storage	the path where will be stored all the information connected with analyzer, if ANALYZER_BINARYSTORE_TYPE is set to filesystem. If you want to mount this folder to some folder on your machine, you can use this instruction in the docker compose: volumes:   - ./data/analyzer:/backend/storage
ES_CHUNK_NUMBER	integer	1000	the number of objects which is sent to ES while bulk indexing. NOTE: AWS Elasticsearch has restrictions for sent data size either 10Mb or 100Mb, so when 10Mb is chosen, make sure you don't get the error "TransportError(413, '{"Message": "Request size exceeded 10485760 bytes"}')" while generating index or indexing the data. If you get this error, please, decrease ES_CHUNK_NUMBER until you stop getting this error.
ES_CHUNK_NUMBER_UPDATE_CLUSTERS	integer	500	the number of objects which is sent to ES while bulk updating clusters. NOTE: AWS Elasticsearch has restrictions for sent data size either 10Mb or 100Mb, so when 10Mb is chosen, make sure you don't get the error "TransportError(413, '{"Message": "Request size exceeded 10485760 bytes"}')" while generating index or indexing the data. If you get this error, please, decrease ES_CHUNK_NUMBER_UPDATE_CLUSTERS until you stop getting this error.
ES_PROJECT_INDEX_PREFIX	string		the prefix which is added to the created for each project indices. Our index name is the project id, so if it is 34, then the index "34" will be created. If you set ES_PROJECT_INDEX_PREFIX="rp_", then "rp_34" index will be created. We create several other indices which are sharable between projects, and this prefix won't influence them: rp_aa_stats, rp_stats, rp_model_train_stats, rp_done_tasks, rp_suggestions_info_metrics. NOTE: if you change an environmental variable, you'll need to generate index, so that a nex index is created and filled appropriately.
AUTO_ANALYSIS_TIMEOUT	integer	300	which sets timeout in seconds for auto-analysis operations to return results after this timeout, so if the request to the analyzer will be running out of time, the analyzer stops processing and returns results to the backend.
ANALYZER_MAX_ITEMS_TO_PROCESS	integer	4000	How many test items can be processed for one request, so if analyzer processes more than 4000 items, the analyzer stops processing and returns results to the backend.
ANALYZER_HTTP_PORT	integer	5001	the http port for checking status of the analyzer. It is used when you run the analyzer without Docker and uwsgi. If you use Docker, you will use the port 5001 and remap it to the port you want. If you use wsqgi for running the analyzer, you can remap the port with --http :5000 parameter in cmd or app.ini.
ANALYZER_FILE_LOGGING_PATH	string	/tmp/config.log	the file for logging what's happening with the analyzer

Environmental variables for constants, used by algorithms

Property name	Type	Default value	Description
ES_MIN_SHOULD_MATCH	string	80%	the global default min should match value for auto-analysis, but it is used only when the project settings are not set up.
ES_BOOST_AA	float	-8.0	the value to boost auto-analyzed items while querying for Auto-analysis
ES_BOOST_LAUNCH	float	4.0	the value to boost items with the same launch while querying for Auto-analysis
ES_BOOST_TEST_CASE_HASH	float	8.0	the value to boost items with the same test case hash while querying for Auto-analysis
ES_MAX_QUERY_TERMS	integer	50	the value to use in more like this query while querying for Auto-analysis
ES_MIN_WORD_LENGTH	integer	2	the value to use in more like this query while querying for Auto-analysis
PATTERN_LABEL_MIN_PERCENT	float	0.9	the value of minimum percent of the same issue type for pattern to be suggested as a pattern with a label
PATTERN_LABEL_MIN_COUNT	integer	5	the value of minimum count of pattern occurrence to be suggested as a pattern with a label
PATTERN_MIN_COUNT	integer	10	the value of minimum count of pattern occurrence to be suggested as a pattern without a label
MAX_LOGS_FOR_DEFECT_TYPE_MODEL	integer	10000	the value of maximum count of logs per defect type to add into defect type model training. Default value is chosen in cosideration of having space for analyzer_train docker image setuo of 1GB, if you can give more GB you can linearly allow more logs to be considered.
PROB_CUSTOM_MODEL_SUGGESTIONS	float	0.7	the probability of custom retrained model to be used for running when suggestions are requested. The maximum value is 0.8, because we want at least 20% of requests to process with a global model not to overfit for project too much. The bigger the value of this env varibale the more often custom retrained model will be used.
PROB_CUSTOM_MODEL_AUTO_ANALYSIS	float	0.5	the probability of custom retrained model to be used for running when auto-analysis is performed. The maximum value is 1.0. The bigger the value of this env varibale the more often custom retrained model will be used.
MAX_SUGGESTIONS_NUMBER	integer	3	the maximum number of suggestions shown in the ML suggestions area in the defect type editor.

Instructions for analyzer setup without Docker

Install python with the version 3.7.4. (it is the version on which the service was developed, but it should work on the versions starting from 3.6).

Perform next steps inside source directory of the analyzer.

For Linux:

Analyzer

1. Create a virtual environment with any name (in the example /venv)

  python -m venv /analyzer

2. Install python libraries

  /analyzer/bin/pip install --no-cache-dir -r requirements.txt

3. Activate the virtual environment

  /analyzer/bin/activate

4. Install stopwords package from the nltk library

  /analyzer/bin/python3 -m nltk.downloader -d /usr/share/nltk_data stopwords

5. Start the uwsgi server, you can change properties, such as the workers quantity for running the analyzer in the several processes

  /analyzer/bin/uwsgi --ini res/analyzer.ini

Analyzer Train

1. Create a virtual environment with any name (in the example /venv)

  python -m venv /analyzer-train

2. Install python libraries

  /analyzer-train/bin/pip install --no-cache-dir -r requirements.txt

3. Activate the virtual environment

  source /analyzer-train/bin/activate

4. Install stopwords package from the nltk library

  /analyzer-train/bin/python3 -m nltk.downloader -d /usr/share/nltk_data stopwords

5. Start the uwsgi server, you can change properties, such as the workers quantity for running the analyzer train in the several processes

  /analyzer-train/bin/uwsgi --ini res/analyzer-train.ini

For Windows:

1. Create a virtual environment with any name (in the example env)

python -m venv env

2. Activate the virtual environment

call env\Scripts\activate.bat

3. Install python libraries

python -m pip install -r requirements_windows.txt

4. Install stopwords package from the nltk library

python -m nltk.downloader stopwords

5. Start the program.

python app/app.py