Thursday, September 28, 2023

What is Smoke Testing?

Did you know that the term Smoke testing has its origin in electronic hardware testing?

It was as simple as looking for any signs of smoke when the hardware under test was plugged in. If you see smoke, then it is clear that the circuitry has some major issue and cannot be tested any further till the problem is rectified.

Drawing a parallel in software testing, once a build is ready to go for QA, an initial test is done to check whether it is worthy to be released for the next level of testing.

Interestingly, there are some other versions of how the term came into existence, you can read about them on Wikipedia by clicking here.

What is the need for Smoke testing?

The purpose of Smoke testing is to identify issues/bugs in the preliminary stages and ensure that a stable build goes to the QA team for testing. This not only makes the testing process efficient but also saves a significant amount of effort and time spent by the QA. The figure below summarizes the process in the simplest possible way.

There are marked benefits to including Smoke testing as a part of your DevOps strategy. Some of the benefits are listed below for quick reference.

Checklist for effective Smoke testing

Smoke testing should be a carefully planned activity. It is always a good idea to have a customized checklist as per your organization’s QA process. In this section, we are listing down general guidelines to be followed. You can add more to this list as per your organization’s testing protocols.

Smoke test all critical functionalities for every build
It is cumbersome and time-consuming for the QA team to work on a build that may throw issues their way, which could have been easily smoked out earlier. Therefore, any new critical functionality or changes to the existing one has to undergo a smoke test for every single build. The thumb rule is to release only a stable build for QA.
Automate whenever you can
Now, a fundamental question arises. If we have to smoke test for every build, then is manual testing enough? Test automation is the answer to this dilemma. Of course, not all tests can be automated. We have debated this at length in our article “To automate or not to automate”. Do take out some time to read it.
The time factor
Time is an expensive commodity. Smoke tests should take minimal time to execute.
Share across the board
Quick feedback and resolution keep the DevOps machinery rolling smoothly. Hence it is important to analyze and share smoke test results across the teams for a faster resolution of issues.
Managing false positives
Test Automation comes with its challenges. False positives are the bane of automation. Ideal automated smoke tests should yield minimal or nil false positives.

How Smoke testing is different from Sanity testing

Does smoke testing sound a little bit like sanity testing? Be assured, it is not. Though it is easy to get confused between the two.We have covered the differences between Smoke and Sanity testing in another blog. Click here to read it. And if you are a bit short on time, below is a summary table that lists down the differences

How Webomates can help in smoking out bugs

Webomates CQ leverages the power of AI/ML to provide a complete testing solution with intelligent analytics and guaranteed execution SLA.

Webomates CQ can seamlessly integrate with your current CI/CD pipeline and can execute smoke tests within 15 minutes to 1 hour.

Let us take a quick look at how we can help in easing Smoke testing for your organization.

Our AI Modeler engine can help organizations in generating and automate the right test cases.

Webomates’ patented AI Defect Predictor can identify false failures with 99% accuracy.

A detailed report along with an in-depth analysis of test results is shared with all the concerned teams. Our ingenious AI Defect creator automates the process of gathering the data for a defect, including the key 20 seconds of video that shows the defect occurring. This helps in a significant reduction in time spent in defect triaging.

If this has piqued your interest and you want to know more about our services, then please click here and schedule a demo, or reach out to us at info@webomates.com.

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Smoke Testing

What Is Smoke Testing

Wednesday, September 13, 2023

Strengthening National Security: The Role of AI Testing in Defense Technology

The fusion of human ingenuity and AI

Gone are the days when you could only rely on traditional methods for safeguarding nations. Today’s defense forces carry out challenging and intricate tasks under erratic and dynamic conditions resulting in an urgent need for modern development and testing strategies.

To succeed, the defense needs to build human intelligence which is aided, enhanced, and augmented with AI and ML capabilities. AI can enhance the testing and quality assurance (QA) processes to ensure improved reliability, precision, and security of crucial defense operations.

Let’s explore the value of AI testing for defense and understand why a strong QA plan is necessary for more intelligent defense solutions.

A Quick Look at the Failures in Defense Due to Lack of Quality Testing

There are numerous examples of potential consequences of insufficient application testing in the U.S. military.

All these errors could have been avoided if the systems were properly tested and validated. According to the Artificial Intelligence in Military Market report, AI in the military market is estimated to be USD 9.2 billion in 2023 and is projected to reach USD 38.8 billion by 2028, at a CAGR of 33.3%.

Priority Outcomes through AI

How can testing solutions help defense? Our objectives and priority outcomes are to:

Unleash defense potential with the power of AI

Through the adoption of AI-enabled testing, our Armed Forces can modernize and rapidly transition into an agile and intelligent force.

Surveillance and threat monitoring

Defense forces capture massive amounts of surveillance data and confidential intelligence from a variety of sources and IoT-connected equipment, such as satellites, drones, radars, and cyberspace. By integrating IoT automated testing into such surveillance and threat monitoring systems, defense forces can validate the reliability of these systems, identify any patterns and monitor potential threats. This allows for effective and proactive defense tactics and increased threat response capabilities.

Enhancing Defense Communications

The defense sector relies heavily on effective communication for successful mission execution, coordination among forces, and ensuring real-time situational awareness.

Testing an ecosystem of intelligently connected devices poses significant challenges.

Functional Testing, which includes Performance testing, Cross-browser testing, and cross-device testing allows the defense systems to undergo extensive testing, minimizing the risk of catastrophic failures during mission-critical operations.

For functional and Usability testing, Webomates has an IoT lab setup for intensive testing of the functionality, usability, accessibility of heterogeneous devices, and networks of these IoT devices.

Accelerating Application Efficiency

Time is of the essence in the defense sector. AI-powered Intelligent Automation Testing solutions will empower the entire force since they will reduce redundant workloads. Defense forces can deploy new systems and updates faster, and also ensure timely response and adaptation to new threats and challenges.

Shift Left Testing speeds up software releases by testing frequently and early in the development process. This method finds issues faster and reduces unexpected outcomes at the end of development.

Mitigating Cybersecurity Risks

One of the critical defense applications for AI technology is cybersecurity, as these attacks can lead to the loss of highly sensitive and confidential data. By leveraging AI testing, defense forces can strengthen their cybersecurity and protect their assets, ensuring that sensitive data is secure and the organization is not compromised

You can take the help of Webomates’ penetration testing, Security testing, Exploratory Testing, and Performance Testing and prevent such cyber attacks.

Strategic Decision Making

Decision making especially in high-stress situations is difficult. And defense forces rely on systems that use AI and ML algorithms to analyze historical and real-time information and interpret data.

These systems need to undergo extensive testing to be able to evaluate risks and help the forces make informed decisions.

Optimizing Resource Allocation

The defense sector works with the motto – ‘Do more with less’ as it operates under strict timelines along with budgetary constraints and must make optimal use of limited resources.

Depending on the requirements of the application, Regression testing along with Exploratory testing can be done on various scales. By pinpointing bottlenecks and highlighting potential improvement areas, they offer valuable test insights into system performance. As a result, defense organizations can optimize their operations, reduce costs, and ensure the most efficient use of resources.

Preventative maintenance of warfare systems

With AI-powered testing techniques, defense forces can switch from reactive to proactive maintenance strategies.

AI testing techniques like defect prediction and self-healing testing can be used by warfare systems including weapons, sensors, navigation, aviation support, and surveillance to identify deviations from expected behavior and take immediate remedial actions. This proactive approach enables teams to handle problems in advance, which reduces downtime and helps them avoid costly consequences.

Secure Software Development and Testing

By automating code validation, deployment validation, and test execution, AI testing can decrease manual effort and improve system resilience as a whole. Continuous testing is an integral part of the CI/CD pipeline, that can be integrated into the defense application’s development lifecycle. Combined with Shift Left Testing, it ensures that the functionality, performance, and security of warfare systems are continuously validated.

Success Story

With our exemplary work with the esteemed US Air Force, we have demonstrated our ability to help organizations achieve scalability and agility while overcoming the typical traditional testing bottlenecks. Webomates has successfully completed SBIR Phase 1 and Phase 2 with the US Airforce.

Webomates’ Testing as a Service (TaaS) – also known as On-Demand testing service – helps you get clear visibility into your testing data, outcomes, and valuable insights by combining applications and data into a single platform.

We work with unwavering dedication to understand your unique needs and provide customized solutions to ensure the success of your application. Take a look at this animation and know the three easy steps you can take to AI automate your application.

To find out more about what Webomates Intelligent Testing services can do for your business, get In touch with us today.

Tag : #AITesting #StrengtheningNationalSecurity #AITestinginDefense

Sunday, September 3, 2023

Generative AI in Software Testing — Webomates

Introduction

The landscape of technology is undergoing dramatic transformations, with Artificial Intelligence (AI) leading this monumental evolution. Among the concepts turning heads is Generative AI, especially its pertinence to software testing. Its potential to transform traditional testing approaches couldn’t be more timely, considering our rapidly evolving digital ecosystem.

Unlike traditional AI which recognizes patterns in the data available and predicts, Generative AI employs algorithms to develop fresh, unseen data based on learned patterns from an original source.

The process of training Generative AI models involves unsupervised learning where the model identifies patterns within unlabeled data, leading to vast and quick data generation, resulting in precise predictions and advanced customer personalization.
Here are some points highlighting how Generative AI is revolutionizing the tech landscape:

Content Creation: The emergence of AI has brought about a transformation in content creation especially when it comes to writing code. By leveraging machine learning techniques, it enables the generation of precise and fast code ultimately expediting project timelines and boosting overall productivity.
Customization: Generative AI algorithms excel at personalization by dynamically creating tailored customer offerings based on individual preferences and past behaviors. This level of customization surpasses immeasurably what was previously possible, enriching user experience while boosting engagement.
Enhanced Product Design: Leveraging generative design allows for innumerable iterations leading to optimized solutions that improve efficiency and usability while reducing costs.
Advanced Problem Solving: Employing generative models can help businesses generate potential solutions for complicated problems more extensively and rapidly than traditional approaches.
Data Augmentation: By generating new synthetic datasets, Generative AI assists in overcoming limitations imposed by inadequate or unbalanced information.
Security Enhancements: It can enhance security measures as its inconceivable behavior provides severe obstacles for cybercriminals aiming to crack coded patterns and systems.

By challenging traditional methods across various domains, including healthcare, cybersecurity, product development, digital marketing — and beyond — generative AI amplifies our ability to conceive possibilities pushing the envelope on technology’s capacity towards unimaginable horizons.

Understanding Generative AI — Roles and functions within this realm

Understanding Generative AI is like stepping into a fantastic sci-fi world where machines don’t just carry out instructions, they conjure up new ideas. Let’s have a closer look.

The roles and functions of Generative AI are varied and fascinating:

–Creator: Just as an artist uses a brush or a writer their words, Generative AI utilizes large datasets to create original content ranging from music compositions to full-fledged articles. It’s every creator’s trusted sidekick.

-Simulator: In need of testing scenarios but short on time? No worries! Simulate real-world situations with Generative AI for robust risk analysis or decision-making.

-Personalizer: Ever wished for your own personal stylist or movie recommendation? Your wish is Generative AI’s command! It tailors experiences based on individual preferences.

Different Generative AI tools in the market

Following are some Generative AI tools that are making waves in today’s digital landscape:

Google Bard is one of the latest projects released by Google, touted as their answer to OpenAI’s GPT-3. An innovative chatbot and content generation system, Bard heavily leans on LaMDA, a leading-edge transformer-based model that aims to foster more open-ended and natural conversations with machines.

Meta Llama2: Llama 2 created by Meta is a language model (LLM) that builds upon its previous version. This state-of-the-art technology has the ability to develop bots like ChatGPT or Google Bard. Its thorough training on datasets allows it to generate coherent and naturally sounding responses, with a human-like tone.

OpenAI’s ChatGPT provides an efficient human-like communication interface facilitating customer service operations but can occasionally misinterpret the context leading to irrelevant responses.Chatbots powered by OpenAI’s GPT, like ChatGPT, are revolutionizing the way we interact with technology. Using advanced machine learning algorithms and a vast database of language data, ChatGPT can generate relevant and contextually meaningful conversations.

DALL-E from OpenAI: Another impressive tool from OpenAI leveraging GPT-3 capability with image-generation models that produce unique images based on simple user-provided text descriptions. By taking simple text input, DALL-E can produce countless unique renditions of the described object or scene, demonstrating its unprecedented ability to combine disparate elements in creative ways.

GitHub Copilot is a cutting-edge AI-powered coding assistant launched by GitHub. It’s designed to help developers write code more efficiently. Using OpenAI’s Codex model, it predicts and offers suggestions for new lines or blocks of code, directly within the editor.

Potential Challenges of Generative AI

Some of the common challenges are listed below:

1. Misunderstanding User Intent: Understanding natural human language can be difficult due to its nuances, sarcasm, and slang leading to confusion or wrong responses.

2. Absence of Contextual Understanding: While GPT can produce coherent replies, it often lacks a deeper understanding of the context which humans provide naturally during conversation. This results in inaccurate responses.

3. Ethical Concerns: There’s the risk of producing harmful, inappropriate, or biased content as they base outputs on input data which may contain such biases.

4. Dependence on Data Quality: The performance of these models is reliant on the quality and quantity of training data. Garbage in, garbage out applies here too.

Addressing these needs continuous advancements in AI conversational models for better comprehension and contextual understanding. Enforcing strict ethical guidelines for AI usage and regulating training data meticulously is required to avoid learning from offensive content.

– Test case generation: Generates various potential scenarios for testing based on a wide range of input combinations.

– Test automation generation: Effortlessly writes scripts to automate repetitive and mundane tasks reducing human error.

– Test strategy updates: Constantly revises test strategies based on new updates or alterations in the project’s scope.

– Test case updates: Adapts generated test cases according to any modifications or new features introduced in app/software.

– Test automation updates: Keeps track of automated scripts for any changes needed due to system enhancements.

– Chat support to customers: Provides real-time responses to resolve customer issues promptly using language processing capabilities.

This revolutionary approach enables efficient bug detection at early stages thus improving overall product quality while saving cost and time.

Infusion of Generative AI in Webomates

Webomates is a pioneer in integrating Generative AI in software testing. With the continual advancements in Generative Artificial Intelligence, the technology has come to play an invaluable role across multiple phases of Webomates’ software testing process.

Test Script Generation Phase

Webomates’ AI engine executes a profound analysis of application workflows, followed by generating comprehensive test scripts that cover every notable functionality.

– Scalability: A considerable number of tests can be generated swiftly covering various facets with no manual intervention involved.

– Adaptability: Adapt to changes easily! Alterations need not worry testers as new test cases detecting these amendments get auto-created on-the-fly!

– Speed: Embracing Generative AI has brought dramatic improvements in speed of test case generation, while scaling back time, efforts and resources significantly.

Test Execution Phase

Webomates’ AI engine can automatically generate diversified and sophisticated datasets for testing purposes. These datasets have the capacity to replicate diverse and multiple real-world scenarios, the manual creation of which could be demanding and time-intensive.

– Random Data: The AI assists in creating random yet relevant data sets which can help check the system’s robustness against varying inputs.

– Edge Case Data: It aids in accelerating edge case validation by predicting situations that seldom occur but have critical implications if overlooked.

Test Case Maintenance Phase

Webomates’ patented AiHealing keeps the test automation always updated by regenerating the test cases within the same cycle, saving precious time and effort.

Embark on a High-Speed Testing Adventure with Webomates!

Generative AI could well be the game-changer we didn’t know we needed until now. So why not let yourself be swept away with progress and embrace these impressive advancements?

Are you racking your brain for expert testing capabilities with impressive turnaround times? Look no further; Webomates is here to sort you out. Our specialized service, Webomates CQ, presents you with the future of testing — Testing-as-a-service (TaaS).

Our TaaS utilizes the power of Generative AI to architect unique test strategies and test cases. We heighten the effectiveness through futuristic AI modules like Defect Predictor for speedy and precision-driven testing.

We resonate with the essence of your business operation, committed to enhancing app quality while amplifying cloud-based test results. Elevate your experience by teaming up with Webomates — because when it comes down to testing, nothing beats expert guidance.

Partner with us to steer your testing regime towards new horizons! Drop a message at info@webomates.com and someone from our team will reach out to you.

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