Download blackleg smoke test

Epidemics of the disease initiated by air-borne L. The symptomatology of the diseases starts with the appearance of gray-green to ash-gray lesions on the lower leaves, when the diseases advances basal stem cankers, small grey oval lesions on the leaf tissue and root rot were observed. Today is more than 37 years since the disease is reported from Ethiopia; however its distribution, current status and pathogen virulence and diversity have not yet known and determined. Since population of blackleg pathogen are complex of at least two genetically distinct groups, now referred to as two different species, L.

The present study have been conducted to see the distribution and status of blackleg disease and to investigate the virulence and diversity of isolate obtaining from major growing areas. Field survey and sample collection were done during the main season of major brassica growing areas of North Shewa, South West Shewa and West Shewa and Gurage zone some places of Ethiopia Figure 1. The zone has a bimodal rainfall distribution and is a typical sub-humid, high altitude agro-climatic zone.

Purposive sampling was applied to select fields following the main roads and accessible routes. Up to five spots per field were assessed. Disease incidence was determined as percentage of plants showing blackleg symptoms out of the plants sampled. The GPS data latitude and longitude were used to plot surveyed fields on a map using computer programmed Arc View 3.

During the survey, growers were asked information on locality name, field history, varieties grown, cropping systems, planting date, weeding practices and disease control practices employed to determine relationship with the disease. Stem and leaf samples showing typical symptoms of blackleg were collected. Samples of infected brassica plants with black leg symptom were collected during the survey.

They were analyzed in Holetta Agricultural Research Center pathology laboratory. Isolation was done from plant parts root crown, stem, leaf, with clear symptoms of the disease. The trial was set up in four replications. Mycelia growth of the isolates was measured in cm on days 5, 10 and For the purpose of dividing isolates and affiliation to their respective groups of the tested fungi L.

Pigment formation was monitored for 4 weeks, based on the changes in color of the agar. During the survey, the highest blackleg severity was recorded at Holeta on station canola type cultivar with severity range of 3. During the survey most of the fields were covered with Brassica carinata variety Yellow Dodola which is under species has BB genome which confers resistance of blackleg. The epidemiology of blackleg differs between location and regions because of differences in climate, growing season, cultivars and especially fungal populations [ 9 ].

During the diseases on set, emergence of brown irregular spots on stem base of rapeseed was observed during the second week of August Figure 2. Although the increase in the number of spots occurred over time, involving a larger tissue area, delayed growth or decay of the infested plants was not observed. Based on the visual observation, only the necrosis of surface tissue was detected during August to October [ 10 - 12 ].

After the test s run, debug artifacts like console logs and videos are saved. Now that we understand our workflow file a bit better, let's run it in GitHub Actions. If you have not already, create a Git repository for your project.

Make sure you have created a repository for your project on GitHub. Then push your code to GitHub. See this GitHub repository for an example. Now go to your GitHub repository and click on the "Actions" tab, which is next to the "Pull Requests" tab. You will see that your tests are running.

This is because our workflow file told GitHub to run our tests whenever anyone pushed to any branch. Click on the workflow run to view details.

Note that the name will vary depending on your commit message. After your test runs, you should see a green check mark indicating that the workflow was successful. You should also see a link to download artifacts video and logs under "Artifacts". Click on this link to download test artifacts. The artifacts are organized with one folder per test. In our example, we only have one test called "myFirstTest. The 0 in the file names refers to the page index. If your test involved more than one page, there would be corresponding logs and videos for each additional page.

Now let's update our workflow file to also run our tests on a schedule. In the ". These lines tell GitHub to run your tests on a schedule specified using cron syntax. Update this value if you would like to use a different time interval. We will change one more thing about our workflow file. GitHub Actions has a storage limit for artifacts, so we don't want to upload them every time. Instead we will only upload logs and videos when the tests fail. Update line 51 from if: always to if: failure. The last piece of our pipeline is an alerting system that lets us know when our tests fail.

In this tutorial we use Slack because it has a free plan. You can also use a service like PagerDuty , which will have a similar setup process.

If you do not already have a Slack account and workspace, create them now. We will now create a Slack webhook, which is a URL that allows us to send Slack messages programmatically. First we need to create a Slack app, which will be responsible for sending our alert messages. Get started by visiting the Slack API website. In the top right hand corner is a green button to "Create New App". Click on this button and you will be prompted to name your Slack app and choose a workspace. In our example, we call our app "smoke-tests".

After you have filled out the form, click the green "Create App" button. You should be redirected to your app's page in Slack. Make sure you are on the "Basic Information" page under "Settings". Under "Add features and functionality", there is a link for "Incoming Webhooks". Click on this link. You will then be able to see the "Add New Webhook to Workspace" button at the bottom of the page.

Click this button to add a new webhook. We will use this webhook to send a Slack message when our tests fail. You will then be prompted to choose the channel where your messages will be posted. In our example, we select the "alerts" channel. After choosing your channel, click the green "Allow" button. You will be redirected to the webhooks page. To test your webhook, copy the code under "Sample curl request to post to a channel". It will look something like the following.

Paste this in the command line and press Enter. You will see the message "Hello, World! Now that we have our Slack webhook, we need to update our GitHub Actions workflow file. We will add a step that makes a POST request to our webhook when the tests fail. Rather than paste our webhook URL into our workflow file directly, we will add it to our repository secrets.

Secrets are encrypted environment variables that store sensitive information. Keeping our webhook URL secret prevents others from seeing it and potentially using it for evil. Add a new secret under your repository settings. The video below provides an example. Now let's update our workflow file.

At the bottom of the ". We changed the value passed to "text" from "Hello, World! If you would like to test that your webhook works, throw an error in your test file ". Then push your changes to GitHub. Your test will fail, and a message will be posted in Slack. You will also be able to download artifacts. In this tutorial we learned about smoke tests and built a smoke testing pipeline. Now you can be your team's smoke testing hero! Smoke testing is a confirmation for QA team to proceed with further software testing.

It consists of a minimal set of tests run on each build to test software functionalities. In simple terms, we are verifying whether the important features are working and there are no showstoppers in the build that is under testing.

It is a mini and rapid regression test of major functionality. It is a simple test that shows the product is ready for testing. This helps determine if the build is flawed as to make any further testing a waste of time and resources.

Learn about comparison Smoke Vs Sanity Testing. The smoke tests qualify the build for further formal testing. The main aim of smoke testing is to detect early major issues. Smoke tests are designed to demonstrate system stability and conformance to requirements.

A build includes all data files, libraries, reusable modules, engineered components that are required to implement one or more product functions. It ensures that all critical functionalities are working correctly or not. In this testing method, the development team deploys the build in QA.

The subsets of test cases are taken, and then testers run test cases on the build. The QA team test the application against the critical functionalities. These series of test cases are designed to expose errors that are in build. If these tests are passed, QA team continues with Functional Testing.

Any failure indicates a need to handle the system back to the development team. Whenever there is a change in the build, we perform Smoke Testing to ensure the stability. Example : -New registration button is added in the login window and build is deployed with the new code. We perform smoke testing on a new build.

Whenever there is a new build, QA team determines the major functionality in the application to perform smoke testing.

QA team checks for showstoppers in the application that is under testing. Testing done in a development environment on the code to ensure the correctness of the application before releasing build to QA, this is known as Sanity testing. It is usually narrow and deep testing. It is a process which verifies that the application under development meets its basic functional requirements. Sanity testing determines the completion of the development phase and makes a decision whether to pass or not to pass software product for further testing phase.

Smoke testing plays an important role in software development as it ensures the correctness of the system in initial stages.