The dawn of Generative AI marks the beginning of a new era, much like the rise of the Force wielders in the Star Wars galaxy. Applications such as ChatGPT, GitHub Copilot, and DeepArt have sparked a revolution, unleashing a wave of innovation and productivity that was once unimaginable.
Generative AI possesses the ability to mimic human capabilities to an astounding degree, leading some to fear its potential to replace humans. However, it’s crucial to recognize that Generative AI is meant to empower rather than replace humans.
Leaders in various organizations are realizing that by fostering collaboration between humans and Generative AI, they can achieve extraordinary feats.
So, what are the potential benefits of this collaboration for businesses?
Generative AI Use Cases
Imagine a scenario where a software application for a galactic banking platform needs testing. Let’s explore how Generative AI can streamline manual efforts, expedite testing cycles, and enhance efficiency across various use cases.
Test Case and Data Generation
Generative AI can assist in generating comprehensive test cases by analyzing application requirements and existing code. It can cover positive and negative scenarios, as well as edge cases.
For the banking application, it can generate test cases for tasks like creating new accounts, transferring funds between different account types, and testing transaction limits. It can create sample customer account details, names, addresses, and other necessary data.
Code Completion
Generative AI can provide developers with recommendations and code snippets to speed up coding tasks. By understanding code semantics and structure, it offers accurate suggestions.
Examples include CodeAI and GitHub’s CoPilot, which utilize contextual understanding and trained models to accelerate coding processes.
Code Optimization
Generative AI algorithms can analyze code to identify performance bottlenecks and propose code refactoring suggestions. For instance, it can pinpoint inefficient algorithms or resource usage, offering alternative implementations to improve execution speed.
Defect Identification and Prediction
Generative AI aids in identifying coding errors and deviations from standards. By learning from previous defects, it can detect patterns and predict potential defects. For example, Webomates’ AI Defect Predictor helps reduce triage time for development and QA teams, enabling proactive defect correction.
Documentation Generation
Generative AI automates the documentation process by extracting data from code, test cases, and other resources. Accenture is exploring the use of OpenAI LLMs to automatically generate documentation, saving time and effort for developers and testers.
How can Webomates Help?
Generative AI is a powerful ally in software testing and automation efforts, akin to the Force in the Star Wars saga. Webomates offers AI-driven testing solutions to optimize testing across various scenarios.
Schedule a demo to unlock the full potential of our AI-driven software testing solutions, which can enhance productivity, accelerate product releases, and propel your business forward. Contact us at info@webomates.com to learn more.
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What exactly is Generative AI?
Generative AI, is the latest buzzword emerging among organizations to enhance productivity and innovation.
Also referred as Generative AI, it expands the capabilities of AI by enabling applications to analyze existing data and create new data that can be in the form of text, images, audio, and video. It uses ML algorithms to train models to understand patterns and data.
In order to provide a better customer experience, teams are increasingly beginning to use the power of generative AI by integrating ML and AI throughout their technology stack.
As per the Generative AI market report, it is anticipated to soar from a USD 8.65 billion valuation in 2022 to a whopping USD 188.62 billion by 2032.
In this article, we explore the transformative impact of Generative AI on QA cost management.
How Does Generative AI empower QA teams?
For every team, there is always one ongoing challenge: how to ensure comprehensive testing while enhancing the quality assurance (QA) capabilities.
Traditional AI systems are programmed to perform specific tasks. Generative ai in software testing relies on models and algorithms that pick up patterns from vast amounts of datasets, and then generate new content and make new predictions.
The introduction of Generative AI in QA provides some innovative solutions to improve productivity, streamline QA procedures, and empower QA teams. Read for more about this blog :https://www.webomates.com/blog/artificial-intelligence/complete-guide-to-how-generative-ai-can-empower-qa-teams/
If you have been reading my blogs and bytes of wisdom, which I occasionally post on LinkedIn, you would know how much I enjoy using a good analogy. So here we go — in Greek, today.
In the epic quest of software development, the teams navigate treacherous terrain riddled with unknown, and sometimes known, bugs and errors. With Test Automation as their trusted ally, they brave the coding complexities to ensure the quality of their products.
But are they making the right choices?
Software testing teams often pick up an easy path — Open-source platforms, due to their customization capabilities and vast community network. Little do they know that they may encounter formidable adversaries in their journey, which may hamper their quest for excellence.
1. Need for Programming Skills: Open source platforms demand a higher level of programming proficiency.
2. Time-Consuming: Creating and maintaining tests can be time-consuming, especially for complex applications undergoing frequent changes.
3. Flaky Tests: Instabilities in tests due to synchronization issues, dynamic content, or environmental variations.
4. Resource Intensive: Dedicated resources may be needed for script development, leading to higher costs.
5. Scalability Challenges: Scaling test automation with open source tools becomes challenging as application functionalities increase in complexity.
6. Limited Reusability of Tests: Test scripts in open-source tools may lack reusability.
To overcome these challenges, the shift to Low-Code/No-Code (LCNC) platforms proves to be the right choice.
Implementing LCNC technologies can revolutionize work processes, automating tedious tasks and allowing teams to focus on strategic, high-value work. According to McKinsey, “There’s expected to be a 70% growth in the adoption of low-code/no-code by 2025”.
Let’s see what power these platforms hold to help you navigate the challenges faced during software development and testing.
Power of Low-Code/ No-Code Platforms
1. Shorter Time to Market: Code templates and pre-built features reduce development time, resulting in a quicker time to market.
2. Reduced Learning Curve: Minimal coding experience is required, enabling easy use of visual interfaces and pre-built components.
3. Accelerated Test Creation and Maintenance: Drag-and-drop functionality and reusable components speed up the test creation and maintenance process.
4. No more Flaky Tests: LCNC platforms minimize reliance on intricate coding, addressing issues related to dynamic elements and reducing flaky tests.
5. Enhanced Collaboration: Encourages collaboration between business analysts and testers, making development and testing cycles more agile.
6. Reduced Costs: Less dependency on developers for UI tests, allowing testers to contribute to automation and reducing associated costs.
7. Scalability: Handles complex test scenarios and scales according to changing testing process needs.
8. Increased Productivity: Developers can focus on more critical business problems instead of spending hours coding.
9. Reusability: Emphasizes modular designs, allowing for the creation of reusable components and test flows.
The power of LCNC platforms is nothing short of a divine revelation. Businesses are harnessing the power of these platforms to elevate their testing rituals and hasten the delivery of their digital progeny. Yet, in this quest for excellence, it is prudent to acknowledge that specialized guardians are still needed to navigate the labyrinths of software development and perform feats beyond the reach of automated tools.
Much like Athena, the goddess of wisdom, Webomates CQ provides codeless test automation services, seamlessly integrating with various automation frameworks. It bestows the ability to modify test cases effortlessly, ensuring a harmonious coexistence between mortal testers and the divine automation realm.
If you want to discover how codeless testing can help you in achieving excellence, reach out to us at info@webomates.com.
You can read the full blog here
It is 2024 and still the age-old question regarding test cases persists: To automate or not to automate?
Much like the mythical creature Chimera, the allure of fully automated testing promising efficiency and resource savings can sometimes be illusory.
If you are one of those who is deliberating on what to do, this post is for you. We have narrowed down so much data and listed these six most important factors that you need to consider before making a decision.
Factors that Influence Automate Testing
Understanding the factors that influence the decision to automate is crucial. Here’s a breakdown that we employ at Webomates in order to make the right decision of identifying the right test cases that can be automated.
Automation Complexity
Not all test cases are created equal. Some can be extremely challenging due to complex UI components or dynamic data, but can be easily handled through manual testing. Automating such cases may require a disproportionate effort, making it more practical to opt for manual testing.
Average Script Creation Time — Ramp up & Maintenance
Automation scripting demands a longer ramp-up time and incurs higher maintenance costs compared to manual testing. Factors like no prior automation background, tool and resource selection, and the time-consuming script creation process contribute to this higher cost. Using Generative AI to write test cases is always a better option.
Desired Speed of Regression
Manual testing, constrained by human resources, faces scalability issues, requiring a significant number of testers or extending the testing period for large test case volumes. Automation in testing excels in delivering rapid regression results. Webomates’ system, for instance, executes a huge number of test cases in just 15 minutes.
Frequency of Releases
The frequency of software releases impacts the effectiveness of automation. In agile environments, where releases are more frequent, the number of modified test cases and broken automation scripts tends to be lower. Webomates’ AiHealing technology will help you in maintaining your test automation up-to-date.
Build Stability
The stability index of the code being regression tested is crucial. If automation scripts consistently break due to significant build instability, their value diminishes. Continuous breakage becomes a bottleneck, consuming resources without providing commensurate benefits.
Addition of Test Cases
This metric gauges the development stage of the product. Early in the life cycle, constant changes and additions may make automation less cost-effective. As a product matures, certain areas stabilize, impacting the value of automation. Constant rework on brittle automation scripts may outweigh their benefits.
Conclusion
If the question “To automate or not to automate” your test cases is still lingering in your mind, then perhaps my blog post could provide the clarity you’re looking for. Check it out!
Competition creates pressure. Today, teams are under enormous pressure to deliver releases with increased speed and quality.
According to GitLab’s research, nearly 60% of survey respondents said their organizations deploy multiple times a day, once a day, or once every few days. Just 11% said they deploy once a month and only 8% said every few months. This highlights how important it is to be agile — both in development and testing.
Top reasons why Enterprise testing fails
Yesterday’s software testing practices can’t keep up with the pace of development and changing business priorities today. Most companies fail to recognize the constantly evolving testing landscape, and the required steps to overcome it.
If your team is struggling, it’s probably because of one or more of these issue
1.Lack of Testing due to Unrealistic Time Expectations
Development teams manage, enhance and validate applications in a short sprinted Agile framework and release them to the end users with limited regression testing. This lack of testing translates to a False sense of Quality — thereby a failure to deliver the fanatical experience they promise.
Regression testing plays a crucial role in achieving a stable version of the software by finding defects before production deployment.
Pro Tip: With its 3 types of regression testing services, Webomates CQ is really quick in initiating the regression in just 3 minutes based on the regression type, platforms, and the target environment selected.
2.Outdated Test Cases & Suite
The number of changes in the testing code is proportional to the changes made by the developer in the application. It is very difficult to identify which test cases should be modified or added.
You need Self-healing scripts that help in test script maintenance by automatically detecting and resolving issues that arise during the execution of test scripts.
Pro Tip: Webomates CQ provides patented AiHealing which is carried out during the software regression cycle within the 24-hour or 8-hour window.
3.Lack of Exploratory Testing
As test case-based testing follows a predefined script, there are chances that a bug may not fall in the script’s scope. To overcome this challenge, you need Exploratory testing using a rotating vector-based approach that pushes the envelope in each regression looking to find new defects in the software release that are outside of the defined test cases.
Pro Tip: Webomates leverages the strengths of exploratory testing on top of test cases in regression testing to expand the scope of the test and take the quality to the next level.
4.Enormous Amount of Time Lost in Defect Triaging
Defects are bound to be detected while boosting feature velocity, but the key is to find them early before production. The best way to achieve this is by setting up a proper tracking system that can identify defects at the right time (as early as possible), triage them, and report them to the concerned stakeholders to improve the quality process.
Pro Tip: Webomates Defect Triaging feature shares a comprehensive triaged defect report that includes defect summary, steps for replicating the defect, a video of actual bug instances, priority suggestions, and test cases mapped with the defect.
5.Not Embracing Shift-Left Testing
The cost of fixing a defect rises exponentially as you move closer to production, along with compromised user experience, functionality, and security. One way to mitigate risks and eliminate surprises is by performing Shift-Left testing — where UI, API, load, and security testing are done early on rather than towards the end of application development.
Pro Tip: This makes it a lot easier to identify and fix the defects in the staging environment itself, enabling faster, better, and quality applications.
6.Engage in Test Automation
Focusing on manual testing can slow down the testing process and lead to inconsistent test execution, mainly when dealing with repetitive tasks or large-scale testing requirements. With test automation, developers and testers can automate the entire build-to-test process across all the stages of software development.
Pro Tip: In this DevOps-driven landscape, it is highly recommended to adopt test automation in order to avail maximum benefit. AI-based test automation includes UI Testing, API Testing, along with non-functional testing.
7.Poor and Insufficient Test Data
The biggest challenge for ensuring an excellent testing process and reliability is the lack of diverse and quality test data. Test data is the generation of data that comes as close as possible to your production data without revealing any sensitive information — all guided by artificial intelligence and analytics.
Pro Tip: Use Generative AI to prepare extensive data sets including edge cases, thereby accelerating testing phases and enhancing overall efficiency. Additionally, you can also use Predictive analysis to create realistic test data by analyzing the existing data patterns.
8.Lack of Cloud Infrastructure Adoption
Managing IT infrastructure involves manual, complex, and mundane processes like setting up the servers, configuring them, deploying the applications, and managing the load whenever required. Lack of cloud infrastructure adoption can restrict scalability, flexibility, and cost-effectiveness, making it more difficult to build and maintain reliable testing environments.
Today, the cloud is a catalyst for any company’s growth. Products are now moving to the cloud, and so is Testing. Testing as a Service (TaaS) — also known as On-Demand testing service — helps you scale with agility and overcome the typical traditional testing bottlenecks.
Pro-Tip: Webomates — a cloud-based testing platform, is powered by a range of patented AI-infused tools. Take a look at this animation and know the three easy steps you can take to AI automate your application.
9.Inability to make intelligent and improved decisions
If you are a manager or part of the C-suite executives, you need data-driven insights that can help you make intelligent decisions. With a focus on solving critical business problems, test insights and analytics that help you improve product quality and effectiveness.
Pro-Tip: Use test insights into the entire CI/CD pipeline to understand the impact every change is having on the product. It also provides guidance and strategic benefits to test management. Read fore about this blog click this link : 9 Reasons Why Enterprise Testing Fails You
Tag : enterprise software testing, Enterprise Testing Strategy, Software Testing, Test Automation, testing enterprise software
Smoke testing essentially checks for the stability of a software build. It can be deemed as a preliminary check before the build goes for testing.
Sanity testing is performed after a stable build is received and testing has been performed. It can be deemed as a post-build check, to make sure that all bugs have been fixed. It also makes sure that all functionalities are working as per the expectations.
Let us first take a quick look at the details of both types of testing, and then we can move onto identifying the differences between them. There is a thin line between them, and people tend to confuse one for another.
What is smoke testing?
Smoke testing, also known as build verification testing, is performed on initial builds before they are released for extensive testing. The idea is to net issues, if any, in the preliminary stages so that the QA team gets a stable build for testing, thus saving a significant amount of effort and time spent by the QA.
Smoke testing is non-exhaustive and focuses on testing the workflow of the software by testing its critical functionalities. The test cases for smoke testing can be picked up from an existing set of test cases. The build is marked rejected in case it fails the smoke tests.
Note that, it is just a check measure in the testing process, and in no way, it replaces comprehensive testing.
Smoke tests can be either manual or automated.
What is sanity testing?
Sanity testing is performed on a stable build which represents minor changes in code/functionality to ensure that none of the existing functionality is broken due to the changes. For this, a subset of regression tests is conducted on the build.
It is performed to verify the correctness of the application, in light of the changes made. Random inputs and tests are done to check the functioning of software with a prime focus on the changes made.
Once the build successfully passes the sanity test, it is then subjected to further testing.
Sanity tests can be either manual or automated. To know more about this blog click on this link
Since its inception, Agile methodologies have played an important role in the software development lifecycle. With digital transformation on the rise, companies are opting for this methodology to achieve progress and model transformation. CIOs played a very important role in bringing agile into different business areas. They understood the need to incorporate the latest technologies for faster results. According to Gartner, “91% of businesses are engaged in one or the other form of digital initiative, and 87% of leaders prioritize digitalization.”
Understanding Digital Transformation and the Role of Agile Testing
Digital transformation is like giving a traditional business a tech makeover. For progressive businesses aiming to digitize, it is important to know that digitalization isn’t a process done once and for all. It is a continuous process that should happen in measured steps. As this principle aligns with agile methodologies, following it would offer better results.
Now, let us talk about the role of agile testing in digital transformation. Agile testing serves as a linchpin, ensuring that the software development and testing processes seamlessly align with digital initiatives’ dynamic and evolving nature.
Benefits of Agile Testing in Digital Transformation
Now that you know how agile testing supports digital transformation, let us see the different benefits it brings.
Adaptability and Flexibility
Traditional vs. Agile
In traditional testing approaches, testing usually happens after the development reaches a concrete stage. Identifying issues and tracing it back to their origins is a major time consuming task at this stage. This can lead to delays in identifying and fixing issues. In contrast, Agile testing is integrated throughout the development lifecycle, allowing for continuous feedback and adaptation.
Digital Transformation Impact
As organizations undergo digital transformation, adapting quickly to changes becomes paramount. Agile testing ensures that testing is a continuous, iterative process, enabling the organization to respond promptly to evolving digital requirements.
Faster Time-to-Market
Traditional vs. Agile
Traditional testing may lead to a bottleneck, due to delays in fixing bugs, hence, slowing down the entire development process. Agile testing, however, focuses on incremental development and frequent releases, ensuring that features are tested and validated continuously.
Digital Transformation Impact
With the increasing pace of digital innovation, organizations need to release software quickly to stay competitive. Agile testing contributes to a faster time-to-market for digital solutions and features.
Collaboration and Communication
Traditional vs. Agile
Traditional testing often involves siloed teams, with limited communication between developers, testers, and other stakeholders. Agile testing emphasizes collaboration and communication throughout the development process. Both, the testing and the development team are involved right from the inception of the project. Any issue identified during the testing is immediately reported to the concerned team for fixing, thus expediting the entire process.
Digital Transformation Impact
In a digital transformation journey, collaboration is essential for cross-functional teams working on diverse aspects of the project. Agile testing promotes collaboration, ensuring everyone is on the same page and aligned with the transformation goals.
Continuous Improvement
Traditional vs. Agile
Traditional testing might not provide sufficient opportunities for reflection and improvement between development cycles. Agile testing incorporates regular retrospectives to identify areas for improvement.
Digital Transformation Impact
Digital transformation requires continuous learning and improvement. Agile testing fosters a culture of continuous improvement, helping the organization adapt and evolve in response to changing market needs.
Customer-Centric Approach
Traditional vs. Agile
Traditional testing may not prioritize customer feedback until the later stages of development. Agile testing values customer input throughout the process, ensuring the final product meets customer expectations.
Digital Transformation Impact
Customer satisfaction is a key driver of digital transformation. Agile testing aligns with this by strongly emphasizing customer feedback and incorporating it into the development process, resulting in products that better meet user needs. Read for more this about this blog : Role of Agile Testing
Requirements traceability matrix (RTM) or traceability requirements matrix is the ability to describe and follow the life of a business/technical requirement in both, forward and backward, directions (i.e., from its origins, through its development and specification, to its subsequent deployment and use, and through periods of ongoing refinement and iteration in any of these phases) – Source Wikipedia
Requirements traceability matrix provides a context to the development team and sets expectations & goals for the testing team.
Requirements Traceability Matrix is a testing artifact that keeps track of all the user requirements and the details of the test cases mapped to each of those requirements. It serves as documented proof that all the requirements have been accounted for and validated to achieve their end purpose. Achieving Requirements tracing and validation intelligently with Webomates
Shift-Left Testing Approach: It is a good idea to introduce traceability as early as possible, instead of waiting for testing results. The developer and the designing team should be notified on a priority basis, as soon as a code check-in triggers the continuous integration process.
Test Automation and Shift-Left testing approach are two pillars of many which render support to DevOps setup. Both of these can also be leveraged upon to incorporate traceability effectively, at the same time addressing the challenge of collaboration and traceability maintenance.
If this has picked your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. We have more exciting articles coming up every week. Stay tuned and like/follow us at
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The exponential growth in digitization programs worldwide has led to a rise in demand for high-quality and reliable applications/products. But, given the current Global economy projections, it won’t be an easy year for businesses looking to ride the wave of digital transformation. The IT QA budget is under heavy scrutiny and the QA managers are under tremendous pressure.
As per Gartner’s study, it is a well-established fact that the businesses that took the bull by the horns in the past and invested in software test automation managed to make faster recoveries.
Then why are some decision-makers hesitant in taking the plunge? Is it their budget constraint?
Little do they realize that this delay cost them dearly in the long run.
Let us examine some of the factors that drive the cost of quality assurance activity thus bloating the IT budgets. Taking pre-emptive measures can salvage the situation before it is too late.
Flaws in test automation
Test automation has proved beneficial for software testing, however, with increasing demand and the need for speed with superior quality has exposed certain chinks in the armor.
False failures are the bane of test automation since they lead to slowing down the whole testing process.
Test suite maintenance increasingly becomes a major headache with every test cycle. Updating test cases based on previous test results, or changes in specifications is a challenging and error-prone activity.
Test data maintenance isn’t easy either. Any change in specifications may lead to modification in test inputs. Keeping pace with rapid changes and testing multiple test conditions with multiple data combinations is an enormous task.
All these issues add to the overall project cost since the efforts involved are high and there might be delays due to tracking & correcting false failures and maintenance activity.
Discovering bugs late in the testing cycle
Sometimes, the bug is discovered very late in the test cycle. Reasons could be any- outdated or limited test data, or unexpected errors due to an untested path because of limited test cases. Discovering bugs late in the testing cycle has a cascading effect on the project quality and schedule, which puts the whole estimated QA cost in peril.
Shift-left testing coupled with the power of AI addresses this issue to a major extent.
Inadequate test planning
Impeccable planning is the key to the success of any project. However, the lack of collaboration and communication between business and technical teams may riddle the project with delays. And the cost goes high since time is money. for more information: Ai based test automation
Ai based test automation or intelligent test automation is a technique or process to automate the repetitive testing tasks using various test automation tools and testing scripts. AI-enabled test set optimizers that build, maintain, run and optimize test assets. Ai based test automation has the capability to heal the test cases by itself thus saving effort and time.
Ai based test automation is the key to continuous testing and has marked benefits in accuracy, scalability, dependability, enhanced test coverage, time, and effort saving. Intelligent automation contributes to the enormous potential for higher productivity, and efficiency in application testing at a lower cost.
Link: https://www.webomates.com/blog/using-ai-based-test-automation-to-optimize-qa-costs/