Test Automation in Production Environment
If you have landed up to this article, I assume that you are an automation test engineer and very well aware of the advantages of automation testing. If not, you can have a look –
This article deals with the automation strategy and implementation in the production environment. As an automation engineer, we set up the test environment as a part of the automation life cycle and run scripts on that. We also have a staging environment that is supposed to simulate production.
In most organizations, staging is a miniature or replica of production and they try to make it in sync as much as possible. It is mandated to test and run scripts on staging and sign off before each release.
Then ‘why to test in production’?
- Testing in production gives confidence that the application is working fine.
- It gives the tester real-time scenarios where the test cases are not predefined and users keep on changing data.
- Edge cases related to network failure, slow connections, high traffic, etc can be found and tested.
- To monitor API response on peak traffic.
- Catch the bugs at an early stage and fix them before they could impact user experience at a much bigger level.
In Organizations, a different instance of the database is kept, synced with production data on a scheduled basis. This further masks the sensitive information like user personal details. The problem with this approach is that often the staging cluster size is much smaller than production. Now the configuration options for eg-load balancer, queues, the size of the thread pool, etc. are pretty different than production. The chances of getting issues in scenarios where traffic is high and multiple systems are involved, become very less in staging. I believe this gives a fair idea of why we must test in production.
Though production testing can be as basic as executing sanity checklist manually or as detailed as we may want. Here I will not be discussing the manual approach. So let’s directly jump on to how automation can help test the production environment. In this article, I will try to answer a few questions related to this-
- The automation approach to test in production
- Risks of testing in production systems
- Advantages of automation
- Tools to automate production test cases
Approach of automation in production- Testing in production is another quality guard around the AUT.
While we should always test at each stage of SDLC, a failure might happen in production. If as a tester we regularly run our automation suite in production, the root cause, and fix is quick and early without impacting the business as it could at a later stage.
- Post-Live Sanity– An approach could be to create a sanity checklist of the most critical functions of the application and create/ reuse the test scripts for them. Having a sanity suit ready and schedule it whenever a deployment is made. This can be further integrated into the CI/CD pipeline.
- Monitoring- Monitoring can be done with a set of functional test scripts scheduled for production. The key to choose test scripts is that those should cover user behaviour and should not manipulate data in production. With the DevOps integration and CI/CD evolution, Many organizations have come up with the concept of ‘ QA in Prod’ which means necessary changes made to the application to support testing in production. Another way is to mock real-time data to run test scripts.
- Synthetic Monitoring- Synthetic monitoring is used to health check the application with respect to actual user behaviour. This is critical to provide seamless user experience by finding and fixing issues as quickly as possible. Synthetic monitoring test scripts are based on past user behaviour data. It tends to find problems users might have with the application. It addresses the following issues-
- Is the website up and running?
- Are the website functions working properly?
- Is there any failure and if yes where it failed?
- What is website performance in terms of API responses?
- Are the 3rd party components working?
- Alerting- Alerting systems are used to send alerts if something goes wrong in production. Alerts can be sent in any form- email, SMS, call, etc. There are many tools available to configure alerts. One of the most popular tools is NewRelic.
Risks of automating in production
The process of testing in production depends on several factors. The major factor is the application itself and what exactly needs to be tested. Test Engineers should keep in mind that there should not be any manipulation of production data. Businesses could experience a loss of transactions or commingling of test data with production data. Experts agree that a major risk of testing in production is the business risk. A bad user experience, security issues or system crashes could all lead to a loss in profit or tarnish a brand.
Major risks are-
- Exposing potential vulnerabilities to the public.
- Loss of data/ Manipulated data.
- Poor user experience due to instability of application.
- Security issues or application crashes.
It is critical to address these risks and design a strategy accordingly.
Advantages of Automation in Production– Automation Testing in production has many advantages-
- Scheduled monitoring and alerting of issues before it can impact user behavior.
- To maintain the desired quality of the application.
- To verify the performance of the application in high traffic and response time of web pages.
- To support continuous testing in agile model.
Tools to automate testing in production- Automation in production may involve-
- Creating a sanity checklist first and write test scripts for the same.
- Creating a monitoring suite with integrated test scripts and integrated alerting system.
- Integrate the automation suit in CI/CD pipeline.
CloudQA’s truMonitor is a complete tool to achieve above requirements.
- Simulate business-critical customer journeys, use the recorded test cases to check critical flows
- Ensure that your web application is available and performs well
- Reduce your hours of manual troubleshooting down to seconds by analyzing hundreds of test executions, thousands of objects, millions of dependencies, and billions of events in seconds.
How does it work?
CloudQA can monitor both your test cases flow and URLs
- Navigate to TruMonitor, Add a test case
- Search and select the test case from the list, set the frequency and save
- Click on the test case to view results
- The results have the following information
- Availability analysis, Performance analysis, SLA report and KPI Trends.
- Execution information – You can set up Notifications for any functional failure during monitoring and also get performance alerts based on the Threshold value.
- Clicking on status will direct you to the step results page.
- Navigate to Monitoring report for detailed analysis of your application.
All the Critical errors in the test cases flow, can be reported to you through SMS.
Talk to out Test Engineers
Fast track your ecommerce monitoring
AI in Testing: The third wave of automation
The evolution of agile methodology enforced the enterprises to innovate and deliver at lightning speed. While delivery cycle time is decreasing, the technical complexity required to deliver positive user experience and maintain a competitive edge is increasing—as is the rate at which we need to introduce compelling innovations.
To meet the continuous integration and delivery needs, we have turned to continuous testing backed by automation but how do we test when these trends continue and gaps widen? As this is the time of digital transformation, we need Digital Testing to meet the quality needs of future driven by AI, IoT, robotics and quantum computing.
If we look at how testing practices changed over time, till 2018 it was focused on CI/CD, scalability and continuous testing.
Now the expectations from testing are more about real-time risk assessment. To cope up the testing expectations in current scenarios, Artificial intelligence (AI), imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.
Beyond Continuous Testing
If we analyze the journey since agile came into the picture, it has completely changed the way applications are delivered. Before agile, there used to be a release in a month or sometimes more than a month. With agile companies are aligned to have a two- weeks sprint and make a release in two weeks. To meet this, Continuous Testing came into the picture where automation suits were developed for regression and sanity testing. This supported quick deliveries and fast-paced testing cycles.
Now as the world is moving towards Digital transformation, the pressure to anticipate market requirements and build a system which is predictive and scalable enough to cater to future trends, going beyond continuous testing is inevitable. Testing will need additional assistance to accelerate the process. AI, imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.
What is Artificial Intelligence?
Lets first understand what does artificial intelligence mean. Forrester defines AI as-
“A system, built through coding, business rules, and increasingly self-learning capabilities, that is able to supplement human cognition and activities and interacts with humans natural, but also understands the environment, solves human problems and performs human tasks. “
In simple words, AI enables machines to learn through data giving them the capability to make a decision. The algorithms are not written to solve a particular problem rather they are designed in such a way to enable the system to make a decision based on data.
How AI can be used in software testing?
Using AI and machine learning to automate-
a) Unit tests – Unit testing is very important to make sure that the build is stable and testable. With AI-powered unit test tools like RPA, a developer can get reduce the flaky test cases and maintenance of unit tests.
b) API testing- API testing saves time and effort by getting into the root cause of the issue. The problem with UI tests is that they are not reliable anymore as UI keeps changing in agile, while API tests give a deeper insight into the application and directly hit the root cause of an issue eventually making the application more robust.
There are many tools which are using artificial intelligence to help take the complexity out of API testing by converting manual UI tests into automated API tests, lowering the technical skills required to adopt API testing and helping organizations build a comprehensive API testing strategy that scales.
c) UI testing- The first step in automation is to convert manual UI tests into automated tests. There are tools which leverage AI to run the test cases on multiple platforms and browsers and also learn from the functional flow, reducing the maintenance effort and making testing more reliable.
Some of the most popular tools are mentioned below-
AI Powered testing tools- There are various testing tools which are using AI, though not harnessing the best of AI, they are still able to help testers a lot-
1. Applitools- It is an AI-powered visual testing and monitoring tool that can run tests on different browsers and platforms. It uses AI to identify the meaningful changes in UI and also identify them as bugs/ desired changes.
It also leverages ML/AI-based for automated maintenance (being able to group together similar groups of changes from different pages/browsers/devices)
2. Testim- It leverages machine learning into the most critical part of automation which is execution and maintenance of tests.
3. Sealights- Sealights uses AI and machine learning to analyze the code and run tests which cover the impacted area. It can be any kind of test- unit, functional, performance, manual, etc.
It provides a useful insight ‘Quality Risks’ which focuses user efforts on the things that matter by letting him or she knows exactly which files/methods/lines have changed in the last build that wasn’t tested by a specific test type (or any test type).
4. Test.AI- Test.AI is building as a tool that will add an AI brain to Selenium and Appium. It was created by Jason Arbon, co-author of How Google Tests Software and the founder of Appdiff. Tests are defined in a simple format similar to the BDD syntax of Cucumber, so it requires no code and no need to mess with element identifiers.
- AI just like a real person, IDENTIFIES the screens and elements in your app.
- AI EXECUTES user scenarios—test on-demand whenever you’re ready
- AI RECOGNIZES elements so that even if things change, your test doesn’t break.
5. MABL- Like the other AI-based test automation tools, MABL can automatically detect whether elements of your application have changed, and dynamically updates the tests to compensate for those changes. You just need to show the workflow that has to be tested and MABL does the rest.
6. Retest- Retest propagates an innovative testing approach, which is a combination of “intelligent” monkey testing and “difference testing” and works actually more like a GUI version management than conventional testing.
This tool does Monkey testing whereby the monkey( is called Surili) is artificially intelligent and can be trained by users by capturing user actions.
7. ReportPortal- ReportPortal, as the name suggests, is an AI-powered automation tool which focuses more on report analysis and management. As per its website it-
- Manage all your automation results and reports in one place
- Make automation results analysis actionable & collaborative
- Establish fast traceability with defect management
- Accelerate routine results analysis
- Visualize metrics and analytics
- Make smarter decisions together
8. Functionlize- Functionlize provides an overall solution for seamless automation with less/no efforts in maintenance all with the help of AI. Its AEA tool finds and fixes the broken test scripts thus eliminating the manual maintenance.
Functionize uses machine learning for functional testing and is very similar to other tools in the market regarding its capabilities such as being able to create tests quickly (without scripts), execute multiple tests in minutes, and carry out in-depth analyses.
It also gives scalability to test suites by maintaining them in the functionlize test cloud.
The machine learning process is completely dependant on the data thus leading to a large volume of the dataset. AI model test scenarios should be equipped to identify and remove human bias which often becomes part of training and testing datasets.
There is a lack of awareness about AI and Machine Learning process and proper training is required to the testers.
AI in 2019 and beyond
As we have progressed from a linear waterfall model to agile, the future is all about AI and machine learning technologies. As a tester, we need to be upfront and start digging more about the various aspects of AI, take the hands-on in AI-powered tools and utilize them.
There are so many places where AI has already paved its way whether it be chatbots or Amazon’s Alexa, we need to be very keen about how we are going to the device out test cases to test such applications and deliver quickly.
With the increasing demand of AI-powered testing tools, testers might need training at earliest. Having advance knowledge of AI and its applications will be very helpful.
Talk to our Test Engineers
Fast track your ecommerce monitoring
API Testing Tools and Automation 101: The Essential Guide
API stands for Application Programming Interface. Typically API is used to facilitate the interaction between two different applications by using any means of communication. When APIs are used over web networks, we term them as ‘Web Services’. In recent times APIs have become the backbone of programming. As in an application, writing APIs to communicate with database, or with another module has become a common practice now and that is why as a tester we must test the APIs to for maximum test coverage.
As a part of integration testing, API automation can help to accelerate the testing and increase efficiency. As most of the companies are using RESTful microservices/APIs at business layer, API testing has become critical component of test plan for any release.
In simplest terms, API is a service which helps two different applications to communicate with each other. Mostly APIs are used to abstract the business logic and direct database access to any application.
Logically we can segregate the entire system into three layers-
- Presentation Layer – This is user interface(GUI) which is open to end users. QA performs functional testing at this layer.
- Business Layer- This is Application user interface where the logic is written. In technical terms this is where code/algorithm resides. APIs come into picture at this layer.
- DataBase Layer- Where application data is present.
In other words the API is the brain of our connected world. It is the set of tools, protocols, standards and code that glues our digital world together. Because of their dynamic nature and capabilities they provide, APIs allow companies to become more agile, things to go mobile, and everything to work together in a streamlined, integrated way.Therefore, API testing is testing APIs at service level and the at the integration level.
Testing Strategy for APIs-
While testing APIs, tester should concentrate on using software to make API calls in order to receive an output before observing and logging the system’s response. Most importantly, tests that the API returns a correct response or output under varying conditions. This output is typically one of these three:
- A Pass or Fail status
- Data or information
- A call to another API
However there also could be no output at all or something completely unpredicted occurs. This makes the tester’s role crucial to the application development process.And because APIs are the central hub of data for many applications, data-driven testing for APIs can help increase test coverage and accuracy.
In testing the API directly, specifying pass/fail scenarios is slightly more challenging. However in comparing the API data in the response or in comparing the behavior after the API call in another API would help you setup definitive validation scenarios.
API testing is one of the most challenging parts of the whole chain of software testing and QA testing because it works to assure that our digital lives run in an increasingly seamless and efficient manner. While developers tend to test only the functionalities they are working on, testers are in charge of testing both individual functionalities and a series or chain of functionalities, discovering how they work together from end to end.
Types of API Testing-
First identify what type of tests you need to perform on API. Like testers do different type of testing for features of their product, same goes with APIs. Commonly testing of APIs include-
Unit Testing– To test the functionality of individual operation. For eg- Google provides geocoding API, to get the longitude and latitude of any location. This usually takes address as input and returns lat longs. Now for unit testing of this API, tester may pass different location and verify result.
Functional Testing- This type of testing mainly focuses on functionality of API. This would include test cases to verify HTTP response codes, validation of response, error codes in case API return any error etc.
Load Testing- This type of test is necessary in cases where API is dealing with huge data and chances of application to be used by no.of users at the same time. This increases the API hits at the same time and it may crash and not able to take that load.
Security Testing- Security testing is particularly critical as API are used to create a link between two different applications. The core purpose of using an API is to abstract or hide the application’s database from other. This may include the testcases like authorization checks, session management etc.
Interoperability Testing- This is to test that API is accessible to the applications where it should be. This applies to SOAP APIs.
WS compliance Testing- API is tested to ensure standards such as WS-Addressing, WS-Discovery, WS-Federation, WS-Policy, WS-Security, and WS-Trust are properly implemented and utilized
Penetration Testing- This is to find the vulnerability of API from external sources.
Web services/ API Protocols-
If we talk about web services there are mainly two type of services or we can say protocols-
REST – REST stands for REpresentational State Transfer which is new on the block as compared to SOAP which means it must overcome all the problems with SOAP. REST is a lightweight protocol which uses URL for all the needed information. It uses four HTTP methods to perform task-
- Get- To get the information. For example getting longitude and latitude in case of location mapping API.
- Post- To insert some data in resource.
- Put- To update the resource.
- Delete- To delete from resource.
REST is more used now a days due to its simple and light-weight architecture.
SOAP API- Stands for Simple Object Access Protocol. It uses XML for message exchanging. All the information which is required to perform this task is given in its WSDL which is Web Service Description Language. SOAP is heavy weight due to its extensive used standards and XML. The main advantages of SOAP over Rest is that it has built in error handling and it can be used with other protocols like SMTP.
Tools for API testing and Automation
There are several tools to test the APIs. When a tester get to test API, they must ask for its document, whether it is a REST or SOAP API or its not-web based API there should always be a document where the details should be written. To approach API testing-
- Ask for Doc
- Write functional or service level cases first
- Write integration tests
- When API is stable enough and passes most of the above tests, perform security, performance and load testing.
- A typical API doc has all the information related to the API like its request format, response, error codes, resource, mandatory parameters, optional parameters, headers etc. The doc can be maintained in various tools like swagger which is open source, Dapperdox, ReDoc etc.
- After that try to write service level cases for API. For example if an API takes n parameters to get the response in which m are mandatory params and others are optional, then one test case should be to try different combinations of parameters and verify the response. Another testcase might verify the headers and try to run API without passing authentication and verify the error code.
- Next comes the step of integration test, where you need to test the API and all its dependent APIs or functions. This also includes testing API response, the data it should return to another API or method and what happens if this API fails.
- Once the API is stable and functional testing is almost done, tester can perform load, security and performance testing.
We often need to automate the testcases which are repeatedly executed. For eg- Regression cases. Similarly in case of API testing, there might be some cases which we need to execute before every release and those cases can be automated.
There are many tools for API automation which are quite popular-
- SOUP UI
- Katalon studio
- CloudQA TruAPI
SOUP UI- It’s very popular tool for API testing.You can do functional, load, security and compliance tests on your API using SoapUI.
Katalon Studio- Built on the top of Selenium and Appium, Katalon Studio is a free and powerful automated testing tool for Web testing, API testing, and Mobile testing.
Postman- Postman is free and helps you be more efficient while working with APIs. It has all the capabilities to develop and test APIs.
Jmeter- Though Jmeter is mostly used for performance and load testing, it can also be used for API functional testing to a good extent.
RestAssured- Rest-Assured is a Java based library that is used to test RESTful Web Services.The library can be included in the existing framework and call its methods directly for fetching response in json format and then perform required actions.
I am taking an example to explain the steps followed for basic API functional testing, here I am using TruAPI tool provided by CloudQA which is new and gaining popularity-
Step1-To run API request you need to first select the Method Type and paste URL of the API. Press Send button to send the request to API or press Add API Test button to save the request-
Try this sample Method Type and API URL
- Method Type: GET
- APIURL: https://um5fdww2pj.execute-api.us-east-1.amazonaws.com/dev/todos
Step2-Information for API request:
- Most of the API require additional inputs to perform the request such as parameters, Headers, Body(JSON), and so on.
- To add parameters of the request you can select the respective Parameters tab and press the Add Parameter buttons to add the required information.
Step3-Sending an API request with authentication:
- In case your hosted API needs an authentication, you can go to the Authorization tab and select the BasicAuth from the dropdown list (Default it is set as Noauth) and then input the Username and Password. You are now ready to send authenticated requests.
- Every API response consists of different values like status code, body, headers, and the time to complete the API request. Below image shows how API response received is portrayed.
- In automation process, it is important that you verify your output using an assertion. To add an assertion in the API Runner, go to the Assertions tab. You can add one or more assertions here.
- Follow these steps to add assertions:
- Choose the response type
- Choose the assertion’s condition
- Input the value to be checked
- You are done adding the assertion
- Variables tab is useful to store the values that are received as a response from an API request sent. To save responses go to the Variables tab and follow these steps:
- Add Variable
- Give a name to the variable for better understanding of the team
- Input the JSON Path of the value to be stored from the response body
- To use the stored value in the variable as expected assertion you can use __name of the variable__ in any other API request.
View or execute a saved API request:
- When you are in API Runner page use View Saved Tests button to view the saved tests
- Select one or more API saved tests and run the selected tests by default the tests shows the last executed run status information
- Results will show up the API execution history
This is a single API execution and automation. For real world scenarios, we often need to create API suit consisting all the regression test cases and run this as a part of regression testing. In agile, it’s crucial to have a suit ready so that it can be integrated with CI and CD.
CloudQA comes with a very rich documentation about the tool, all the tools provided by CloudQA are aligned with the idea of “Codeless automation” and very easy to use for manual testers.
Link for documentation- https://doc.cloudqa.io/TruAPI.html
LIKE THIS POST SHARE IT WITH YOUR FRIENDS
Enterprises use TruAPI testing and monitoring solutions.
Enterprises use TruAPI testing and monitoring solutions.
CloudQA – Why User Experience is of utmost Priority to Deliver Quality
As a product based firm CloudQA often in its demo sessions is hit by a query – How do you assure quality to the digital audience each time? Our answer is simple – We value user experience more than the code. Our testing approach is user-centric, and if research shows users are deviating from the traditional approach, we improvise and align our testing strategy with them. Our Review and Fixtures models make sure to explore the product development Lifecycle, fix the testing approach and rearrange the components. How?
Read on to know How We do it…
Recognize the Transformation
The users are “SUPERPOWERFUL” they can push a product to its High and could even let it shatter on the ground. Many of the Founders put in their efforts to research if the idea is worth to be converted into business, but most of them forget the packaging of the idea. Does that serve the purpose? Many firms try to get users to try their beta version before rolling out it to the users, but is it not too late to put your product under stress? What if it fails?
Would you like to go for a desktop version or a mobile app as well? Would that be available only for Android or even iOS? Would open source tools be the right choice? Could these be integrated seamlessly with other third-party tools? These all questions may have a different answer when approached via the budget and timeline the firm has. But think about it from user perspective – Are the technology used safe and secured for the users? Would users be more inclined to a web version or an app would be good-to-go? Could constant monitoring be more helpful in predicting disaster before it happens?
The timeline is another crucial aspect of recognizing the change. Users need things at a super-fast speed. They are not willing to wait for your monthly release; they need to be updated software version on a daily basis [at least].
Users no longer wish to provide Name, Age, email address type data set again and again. The software should be smart and intelligent enough to pick it up and allow users to log in and showcase personalized dashboard/preferences.
With these known transformations, the traditional product development Lifecycle needs to straighten out. Currently, the three categories of Product Development Cycle that are floating are –
- Traditional monolithic desktop application – Products like MS Office or Chrome browser that could run independently on a desktop.
- Core Services – These are the software pieces that are in the form of an API and mostly need an integration. Just for example – payment, storage, ad networks, analytics
- Standalone yet Integrated Applications – These are the products that are mostly user facing and could be integrated with any other third-party products or core services. They could work independently but may also be integrated with others. Just for example an online food ordering joint may work independently but could also be combined with Google Maps to know your location and provide you with personalized choices.
Now we know “the transformation” and the “product development category” so now putting up a test plan be an easy job? Not Really! Albert Einstein said
You can never solve a problem on the level on which it was created.
Hence each application needs to move at a different level to be tested.
Reviews and Applying Fixtures
If you already have a Test Automation suite we run a “Quick Review” to know why is it breaking. Based on our experience and research here are some of the common reasons for why test automation is breaking –
- Low User Engagement – Based on Quettra’s data shows that 77 percent of users never use an app again 72 hours after installing. After a month, 90 percent of users eventually stop using the app, and by the 90-day mark, only 5 percent of users continue using a given app. All this data highlights an interesting fact that your app is not engaging enough. Some of the reasons highlighted by users are – App Crashing, Poor performance and usability and excessive use of memory.
- Halted continuous delivery and DevOps – Another research shows that only 8% teams could achieve nearly 50% of test automation and 41 % of the teams had less than 1% test automation achieved. This data highlights the fact that even though the efforts were made but were not continuous and did not involve DevOps.
- The Huge cost of testing – Testing needs tools and resources that come with a price tag. Hence many firms cut the budget to save pennies and roll out bad quality product.
- Gaps between business owners and QA – There exists a huge gap between the product owner specifications and testers viewpoint that reflects the quality of the product.
Once we have identified the “problems areas” we apply Fixtures –
Ways to fix to test broken approach
@CloudQA we try to provide a remedial solution to patch up the Test Automation Suite to enable in offering a quality product. Here they are –
Be the User
Have you ever thought how a knee replacement device could be tested? As a tester, would you cut your leg and then test it? Well, not really but the feel of the user is of utmost important for a tester designing the test cases. And these scenarios cannot be achieved by just following a High-level requirement document; you need to think and act like him.
Scope of Testing
Testing is not restricted to functional document, based on usability, performance and even security are the key aspects that need to be covered in the scope of work. In fact, our recent article on why testers need to be ethical hackers gives you top reasons to cover security aspects for sure.
Automate All Processes
While a single sign in is a user preferred choice, make sure to apply it to automation process as well. Single click should result in test case execution, review test results and analyzing the root cause. So go beyond test execution…..go for automation of all processes.
As a user, how/what would you rate a functionality of an app? Would the user have enough information to know about the fields? Does the UI looks scatter? Try to challenge yourself as a user, and you could surely give the best experience they ever had.
I remember my 8th-grade economics lesson – democracy is
For the people
By the people
Of the people.
And trust me that’s the same about your product. Believe in the power of users!
LIKE THIS POST SHARE IT WITH YOUR FRIENDS
Talk to out Test Engineers
Fast track your ecommerce monitoring
Top Five Reasons Why Testers Need to Be “Ethical Hackers” To Stop threats like WannaCry
Cyber threats and data security are one of the first concern of any firm. As an organization, what do you do to save yourselves from cyber threat? Firewalls? Anti-virus? Or Setting up processes and educating employees? Hiring a security firm to audit your processes and conduct penetration testing? What else could be done to prevent Black Hat Hackers?
Have you ever thought of asking your QA team to explore the vulnerabilities of your system in an ethical manner?
We @CloudQA give you top five reasons to do so –
When an in-house team is available to extend their roles, which would be more cost-effective than hiring a security agency to perform the same function.
Once the internal QA team is equipped with the checklist, the checks or penetration testing could be scheduled at regular intervals making it a continuous process, thereby enhancing the quality of the product.
Access Provided to in-house teams only
The data, servers, infrastructure would only be accessed by the in-house team making it leak-proof. In the case of any data theft or damage, the person could be tracked easily as who caused it.
In-house Testers/Hackers Means Long Commitments
Being in the same environment like yours, one would understand the criticality of a product. Hence he/she may devote much time and energy to discover the loopholes.
In-House Team means better Stability and Back-up
An organization backed up with a skilled team set is a solid foundation for stakeholders. Just imagine a technical breach, and with the in-house team, you could get it resolved faster then, looking for outside help.
Testers could explore new skills
While manual testers are going through the tough time saving their job, it’s time for them to add some new skill set to their profile. Test Automation is on top of the list amongst the skillset, how about adding ethical hacking? With Ethical hacking added onto your resume, who knows if you could trace down one of the biggest loopholes in a system.
Technologies like Artificial Intelligence, Blockchain, IoTs are knocking the doors of every firm, making it more complicated for a layman but much easier for a Black or Grey Hat hacker to get in. You can keep guards and surveillance to watch for, but do you know the big hole inside your house that could let thieves in? So, get your QA team ready and let them explore the house as Ethical Hackers performing penetration testing and stop the threats like WannaCry, RedOctober, Wiper,Shamoon.