Tuesday, May 16, 2023

How ChatGPT is revolutionizing Software Testing

Welcome to the new world of ChatGPT.

The world is on the cusp of a major revolution, with the rise of generative AI technology. And at the heart of this revolution is ChatGPT, an advanced language model that is capable of creating code, detecting code vulnerabilities, crafting poetic verses, and even clearing competitive exams. In just a few short days since its highly anticipated release, its versatile nature has already left users impressed.

How can ChatGPT help in Automation Testing?

We are currently riding a wave of business transformation where we are merging ChatGPT with every business. With its remarkable ability to understand natural language and generate context-specific responses, ChatGPT can provide valuable insights for QA teams and is poised to become a game-changer in the world of software testing.

It can assist you in creating a test strategy by providing a wealth of information and insights to help ensure that the testing process is well-planned, effective, and aligned with the project goals and objectives.

Let’s see how we can effectively utilize ChatGPT to create test cases, test strategies, and lots more:

1. Generate test cases

The process of creating test cases is a tedious and time-consuming task. It requires a keen eye for detail and an in-depth understanding of the application or feature being tested. With ChatGPT’s assistance, this process can be made simpler and more efficient.

By analyzing the input provided, it can generate test cases, and recommend input values and expected outcomes, helping to ensure that the testing process is thorough and comprehensive. Additionally, ChatGPT can offer best practices, sample test cases, templates, and examples based on your requirement for designing efficient test cases.

When it comes to testing software products, it’s not just about creating a list of test cases. It’s also about ensuring that the test cases cover a broad range of positive and negative scenarios. You can also take help of ChatGPT to create a set of positive and negative set of test cases.

In addition to creating test cases, ChatGPT can also help you to refine and optimize the existing test cases. It can suggest improvements to existing test cases, making them more effective and efficient.

2. Generate UI Automation Script in different languages

Although ChatGPT does not generate UI automation scripts directly, it can provide invaluable support to automation engineers by generating detailed and well-structured test cases.

These test cases can be used to build UI automation scripts for C#, Java, Python, Ruby, and other programming languages using tools that support those languages.

3. Test result analysis and reporting

One of the most important steps in assuring the quality of a software product is analysing test results. ChatGPT can assist in the analysis and sharing of test results, offering insightful information on possible areas for improvement or failure analysis.

You can prompt the tool to include metrics like test coverage, test execution times, and the number of defects found in the report. Furthermore, as test results accumulate over time, ChatGPT can also assist in noticing patterns and trends. This can help you to use this information to pinpoint areas that may need additional time or effort, enabling you to focus efforts on the most important areas first. Read more about:https://www.webomates.com/blog/how-chatgpt-is-revolutionizing-software-testing/

One of the critical reasons for software release delays is the time consumed in maintaining the failed test cases and inability of current testing system to self heal the test automation. Test automation is proving ineffective and outdated (Automation is always out of date) due to the time and cost involved in maintaining these failed test cases. As per Webomates’ study, the cost of software test automation is only 20% in the setup phase, and the cost of maintenance of the software test automation is 80% of the overall cost.

The broken test cases and scripts disrupt the software release cycle, and analyzing the root cause of broken test scripts is even more time-consuming. According to a study by IBM, searching, managing, maintaining, and generating test data encompass 30%-60% of the tester’s time. AiHealing bridges the gap as it not only identifies the changes in the test cases and automation scripts but also adapts them in real time. AiHealing is playing a vital role in faster software release.

What is AiHealing?

AiHealing is Webomates’ patented technology that efficiently reduces the time and cost of maintaining failed test cases. AiHealing is a 3-step process depicted in the figure below:

Photo of Self Healing Test Automation by Webomates Inc

Step1: Identifying the Defects

The AI system analyzes the pass/fail report and other execution data and identifies false failures. AI system takes two category inputs:

Input: Test Automation execution data: The most apparent entity, in this case, is the Pass/Fail report and exception data results for this execution run. However, other data such as locators, test data inputs, timeouts, console logs, and network logs can also help in AI decision-making.

Input: Build Release Notes: This is the text that describes the features in the build. These are the User Stories stored in a system like Atlassian’s JIRA, which contains the text describing the features or bug fixes that are in a particular build.

Step 2: Root Cause Identification

Once the AI Analyzer detects the required changes, it moves to the next step of identifying the Root Cause for the failure of Automation Scripts.

Script Change: From a process perspective, the AI system analyzes the pass/fail report and other execution data and identifies false failures. Then it recognizes issues such as application changes, features added, and newly added test cases and does a root causes analysis. The issues could be due to locator changes, script errors, timeout errors, and feature changes. These items alone cause 60% of the false positives. Our AI system not only identifies test data changes but also end-to-end flow changes. Additionally, we have release notes/user stories that help identify the test cases that need healing. After our AI Defect predictor recognizes the root cause, it proposes the next course of action, i.e. healing the test case. For More Information Visit – www.webomates.com

Test Scripts once created are automatically executed while doing any new releases. Test Automation often breaks down in case of any new feature release or any changes in locator

AiHealing bridges the gap as it not only identifies the changes in the test cases and automation scripts but also adapts them in real time. Webomates’ AiHealing is a process of Self Healing Test Automation and thus helping in faster releases

Webomates’ patented AiHealing Test Automation promises to give 100% results of the evaluated quality test cases with no false positives. So no more setting up and modifying software release timelines back and forth. Partner with us for a stress-free software release experience.

#Webomates #AIhealing #TestAutomation #Softwaretesting #TestAutomation #SelfHealingTestAutomation #AITesting #intelligentestautomation #aitesting #qa #agile #devops #shiftleft.

Monday, November 21, 2022

E2E Testing | End-to-End Testing

End-to-End testing, also known as E2E testing, validates the flow of entire software from the beginning to the end along with its interactions with entities like hardware, external libraries, third-party plugins, databases, etc.

A good quality assurance process must include comprehensive End-to-End testing. Let us understand why.

1. Verification of application health:

End-to-End testing validates every functionality of the application right from the UI to the backend. It not only provides an overall perspective of the application in terms of how well the requirements have been realized to deliver the expected end product, but also checks for the performance and security aspects.

2. To check the system flow:

Software applications are getting increasingly complex and that is why testing has to be comprehensive to cover all possible scenarios. End-to-End testing ensures that the process and data flow is smooth for all the subsystems. There is no margin of error here, since the failure of flow at any point may lead to the failure of the whole system.

3. To verify the backend and its interaction with the presentation layer

Complex applications working on colossal databases coupled with dynamically changing inputs are a perfect recipe for complicated test scenarios. End-to-End testing helps in verifying data integrity and data exchange with the presentation layer in conjunction with the entire process flow.

End-to-end testing also known as E2E Testing is used to validate the functionality of an application.

It ensures that all the features of the application are working properly before releasing it to users. The term ‘end-to-end’ refers to the fact that the test starts from the user interface and ends when the application interacts with the outside world.

End-to-end testing can prove to be very valuable to organizations focused on delivering a consistently high-quality product to their customers. The value attached to End-to-end testing is significant and proves beneficial in the long term.

#webomates #e2etesting #completetesting #endtoendtesting #testautomation #e2etestingcompany #e2ete #stingsoftware #e2etestingtools #intelligenttestautomation #softwaretesting #softwareqa

To know more information visit: https://www.webomates.com/blog/a-simple-guide-to-end-to-end-e2e-testing/

Thursday, November 3, 2022

QA Ops with Intelligent Test Automation

QA Ops is the next-level process of DevOps and is integral to the development and delivery operations. Its prime objective is to introduce quality from the onset of any project and ensure that all defined quality norms are followed throughout the development and delivery process.

QA Ops framework is the interlinking of three major processes of the development and delivery cycle — Quality Assurance, Development, and Delivery. This further implies that the concerned teams have to strive for continuous improvement to ensure that a high-quality product is delivered to the customers. Everyone involved from the conceptualization to realization of the application is accountable for maintaining the quality throughout the process.

The success of QA Ops is based on a higher level of collaboration and communication between all the stakeholders. The QA engineers need to work closely with the development team and operations team to decide on QA strategies and make sure that they are followed stringently.

In simple words, QA Ops helps in integrating continuous testing in CI/CD pipeline.

4 Key elements of QA Ops

The following diagram represents 4 key elements that need to be incorporated to embed QA in the core of operations.

Automation
Automation accelerates the quality feedback loop, by creating, executing, modifying, and self-healing the test suites. Every build has to undergo functional testing, performance testing, and smoke testing, besides the customized set of tests specific for the module under test. Conducting these tests can be challenging if not conducted smartly.
Intelligent automation can save precious man-hours and costs by fast-tracking the whole testing process.
Parallel testing
Parallel testing involves executing multiple test cases on a module across various test setups in tandem, at the same time. Needless to say, these tests have to be automated for ideal parallel testing.
Parallel testing is important to save time and accelerate the CI/CD pipeline by identifying any flaky tests. The involvement of all the teams ensures that these tests are immediately traced back to their origin and appropriate action is taken.
Scalability
Scalability aids in determining the application’s performance under varying load conditions. QA Ops requires to have a test framework and infrastructure in place to conduct scalability testing as and when required.
Integrating development and QA with operations
The QA team, the development team, and the operations team need to work together to define the whole QA Ops framework. Involving all the stakeholders from the beginning ensures that clear quality processes are defined from requirement understanding to delivery. Read For More: Intelligent test automation

Wednesday, September 14, 2022

Code coverage criteria | Achieve Effortless Code Coverage

Code coverage criteria is software testing metric that determines the number of lines of code that is validated during a test procedure, which in turn helps in analyzing how comprehensively a software product has been verified. In short, it allows organizations to evaluate their code to identify its

Photo of Code coverage criteria | Achieve Effortless Code Coverage by Webomates Inc Software testing assesses application quality. Do we need to assess testing quality as well? Yes, the test strategy, assets, and practice have distinct quality aspects. Some of the aspects that we can examine the testing quality are listed below:

Testing Scope: The depth and breadth of the application areas covered and test scenarios covered.

Relevance: Significance of the areas covered during testing and the defects found.

Efficiency: Result and test output in terms of defects as per the time spent.

Efficacy: Defects found per test effort (creation, maintenance, execution, analysis).

Drag: The time spent holding a release while it is being tested, non-tester time invested in test asset creation and review.

Reporting and Analysis: Clarity of defect reports, meaningful analysis.

This article focuses specifically on the quality of application scope. The methods we can use to assess scope are threefold:

Requirements traceability: Mapping of application features, functions, use cases, and or/scenarios to test cases.

Test artifact review: Manual review of test suites, test cases, test data, and applied judgment.

Code coverage: Automated assessment of application coverage.

These three methods are complementary and should be used together.

Code coverage has the advantage of being objective since it is provided by a computer and cannot be disputed or subject to opinion. Furthermore, the heavy lifting is done by the computer, although a little labor is required to set it up using the tools available.

So code coverage is an important mechanism for assessing one aspect of test quality. It provides several types of quantitative metrics at various application code hierarchies, as well as qualitative data — displays of annotated source code, showing lines exercised and not exercised.

What is Code Coverage

In simpler words, “Code coverage is software metrics for the number of lines, branches, and conditions of code exercised during the execution of your test suite.”

Any line of code that has not been validated is potentially a ticking time bomb waiting to go off. In a way, code coverage is a preemptive measure to find issues before the code goes to production. Additionally, code coverage is extremely vital for understanding parts of the code that are rarely executed, such as code for handling exceptional and atypical scenarios.

Good Code coverage is the result of well-defined QA Ops practices and is your safety net that prevents disasters. Inculcating good design, execution, and quality process from the onset is the way to go.

Code Coverage Criteria

There are multiple criteria used by developers and test engineers to ascertain the code coverage criteria. The most commonly used are listed below.

Function coverage: The number of functions that have been called.

Statement coverage: The number of statements that have been executed.

Branches coverage: The number of the branches of the control structures, like if-then, that have been executed.

Condition coverage: The number of Boolean sub-expression tested for a true and a false value.

Line coverage: The number of lines of source code that have been tested.

Besides these, depending on the type of product Finite State Machine coverage, transition coverage, register toggle coverage, etc. are also considered while calculating code coverage.

Value of Code Coverage

Code related

Identifies bad and dead code: Code coverage analysis points out unused legacy code which can be removed to keep the code clean. It also helps in improving coding practices by pointing out loopholes.

Helps in identifying code segment omissions: Code coverage helps in identifying the code segments which may not be executed just because the conditions were not “right”. It is imperative to test every segment to improve the quality to prevent any surprise bugs.

Test related

Early defect detection and prevention: Early defect prevention helps in managing your technical debt better. We have covered test analytics and its value in detail in our blog “Intelligent Analytics with AI-based test automation”. Read it to understand how defects discovered late in the project life cycle can create havoc.

Helps in identifying test omissions: Code coverage aids in creating new test cases to increase coverage by identifying untested code segments.

Gives a quantitative measure of code coverage: Quantitative measure of code coverage is a number that raises a red flag if not in line with the set target. It sets the wheels of analysis and damage control in motion.

Measures efficacy of test suite: Code coverage helps in analyzing how comprehensively a software is verified and how well the test suite was designed to cover all possible test scenarios. Click to know more about this article: Code coverage criteria

Monday, September 12, 2022

Bringing AI in Testing: A CEO’s role in handling this shift

How CEOs think???

With AI, you can now think the unthinkable.

Development today is at warp speed. Hourly, weekly, monthly releases are the new norms. This demand for rapid pace of development and quality assurance puts an onus on the CEOs and decision makers.

C-suite leaders need to recognise that AI is the essential difference that will help them not only tackle a wide range of challenges but also help them keep their firm competitive, lucrative, and resilient.

Applications are becoming too complex for the traditional testing methods. They are time consuming and lack necessary new-age technological skill sets. Scalability to changing requirements is nil, it’s slow and also incurs high capital costs such as expenditure on hardware, software, maintenance, and hiring the right resources.

Leadership demands vision! What if you could test what you want when you want? What if you could discover defects early on? What if that helped you release new products frequently? And what if you could do this all day, every day!
According to the IBM research survey, over 3,000 CEOs ranked IoT, cloud computing, and AI as the top three technologies expected to help deliver results.

The CEO’s role – Think what AI and innovation bring to the table!

Revolutionary Cloud Based Testing Platform - Webomates CQ

With Artificial Intelligence and Machine Learning technologies, CI/CD model, Agile, and DevOps practices being used by companies across all domains, the push for high-quality product release through rapid release cycles are at an all-time high.

AI can assist leaders in staying one step ahead in the competition and be resilient to market volatility. As a CEO, you need to invest in solutions that help you scale with agility and overcome the typical traditional testing bottlenecks.

Here are the top 5 actions of CEOs that will help them improve testing:

A Right Partner – Testing at Speed with Webomates

Webomates is the key to unlocking the innumerable AI Testing benefits! More and more companies are acknowledging the axiom of AI Testing and are working diligently to reap its benefits.

1. Power your strategic agility with Shift Left Testing

Expecting to develop a ‘zero-defect’ product is over zealous. But expecting to predict a defect/performance glitche and resolve it – that’s totally possible!

Shift–left testing allows you to enjoy the best of both worlds – test your application right from the beginning and also have a high–performing application at the same time. It is a continuous endeavor in collaboration from development and QA teams. By testing early and often, Shift Left Testing mitigates risks and eliminates surprises by moving the testing towards the early stages of application development. The goal here is to test each feature the development team develops and provide quick defect data so that they can fix it.

By rethinking testing and seeing it as something that should occur not only in production environments but also at an earlier stage of the software delivery chain, organizations can ensure ROI of the time and energy that their software delivery teams put in. It helps you get past the roadblocks with which they’ve traditionally struggled as they attempt to embrace the Agile and DevOps practices.Read for More : Bringing AI In Testing