How can AI be used to test mobile and web applications in software development?

AI can play a significant role in web and mobile testing by automation various aspects. some of them are: - Automated Test Generation - Test Script Generation - Test Data Generation - Dynamic Test Environment Configuration - Natural language process testing docs - Self healing testing - user behaviour simulation testing

Using AI for testing Mobile and web applications is a smart way of creating the desired results in Software Development life Cycle. It births products and projects with an advantage over Manual testing. It is the future however caution should be applied especially when we exchange our technology with artificial intelligence without restrictions.

AI can be a valuable tool for testing mobile and web applications in software development. It can help streamline and improve various aspects of the testing process, making it more efficient and effective. Here are several ways: Test Automation Test Data Generation Test Case Generation Visual Testing Code Analysis and Static Testing Continuous Integration and Continuous Testing Predictive Analysis Natural Language Processing (NLP) Anomaly Detection Test Reporting and Analysis It's important to note that while AI can enhance the testing process, it is not a replacement for manual testing and human expertise. AI should be used in conjunction with traditional testing practices to achieve the best results.

Here is my experience and usage of AI tools in testing and devops - Helps in predicting failures in the automated pipelines. - Generate automated scripts based in business acceptance. - optimize the existing scripts to increase stability of automated scripts. - It helps in advances the usage of single scripts across multiple environments with configuration policy. - Aligns the test results across multiple iterations to decide on stability of the AUT. - Boost the confidence of each production releases by comparing the historical test results.

AI can be used to generate test cases and save lots of time typing. You can also have your IA tool put those test cases into whatever format it needs in order to import them into your test case management tool. As a tester, your job is to verify that the test cases are correct and modify them as needed. AI is also useful for generating code. I like to use ChatGPT to wrap HTML objects in C# classes for use in my Selenium tests. It speeds up creating automated tests.

1. AI can automatically generate test data or input values for test cases. This data can include various scenarios to assess the software's behavior under different conditions. 2. AI can generate test scripts and cases based on predefined testing objectives, specifications, or testing data. These scripts can encompass functional, performance and other types of tests. 3. AI can execute test cases automatically, simulating user interactions with the software. This includes UI interactions, API calls, database operations etc. 4. During test execution, AI can identify defects or unexpected behavior in the software. 5. Some AI testing tools have the capability of self-healing to adapt to changes in the user interface or functionality.

According to me Human Intelligence cannot be replaced with AI, especially in the areas of quality control, both functional and NFR. As of now AI doesn't possess the intelligence to perform Quality Control. May be we can use it for generating Test data (not sure if there are any tools for generating TD already) . In future there is a possibility, if the business logic is designed in the BPM kind of application and strictly followed.

In accordance with common STLC steps AI Testing has some of the following benefits. First in Requirement Analysis it can help us understand the business needs from the NLP processing itself rather than huge documents. In Test design it can help us suggest the test scenarios and help in prioritization as per AUTs user behaviors and indirectly to test plan. Then during execution smart tools can help focus on the dynamic test case execution, real time AI data generation to mimic live production models. During defect process triage the system specific reasons and consolidate and feed us priorities and severities. Even in retest it can foresee future defects. And in closure provides us meaningful reports and results

With AI testing, the team can use automation tools that leverage AI algorithms. These tools can simulate user interactions on a wide range of devices and configurations simultaneously. AI can identify issues such as crashes, UI glitches, or performance issues across the app. It can also collect and analyze data on how the app performs under various conditions and adjust its testing strategy as new devices and operating systems become available, ensuring the app remains compatible. Overall the benefits are evident.

AI can run a vast number of tests simultaneously, create multiple test scenarios(which are time-consuming or can be missed by humans), generate test data, provide comprehensive coverage and identify defects in various aspects of software functionality. In some cases it can auto-heal the test according to the code changes.

AI testing could provide automation for some testing tasks and activities, as well as reduce the time spent on execution. It could potentially identify other/new bugs that were not captured by the tester. We need more information to understand it better.

The benefits of AI testing include improved accuracy and reliability in evaluating software, faster testing processes, increased test coverage, and the ability to handle complex, repetitive tasks. AI testing also enhances the detection of subtle defects and security vulnerabilities, ultimately reducing costs and ensuring more robust and trustworthy software.

Challenges in AI testing include: Data Quality: AI models rely on high-quality data, and obtaining and maintaining such data can be difficult. Bias and Fairness: AI system may inherit biases from training data, requiring careful evaluation and mitigation. Interpretability: Understanding and explaining the decisions made by AI models can be complex. Evolving Models: AI models evolve, necessitating continuous testing and retraining. Security Concerns: AI systems can be vulnerable to adversarial attacks, making security testing crucial. Resource Intensiveness: AI testing often requires significant computational resources. Regulatory Compliance: AI systems must adhere to various regulations, requiring compliance testing.

Implementing AI testing may require specialized skills and expertise. Understanding AI algorithms, data models, and testing frameworks can be challenging, requiring dedicated training and resources. AI testing systems require continuous maintenance and updates to adapt to evolving software environments, changes in testing requirements, and emerging technologies. Also AI testing may raise ethical concerns, particularly related to privacy, security, and fairness.

Rather than making the test decision on AI the testing methods and tools can be AI supported. For eg instead of leaving the AI to test the application. We should have the capabilities to inform the business process and test this in the AUT and the AI should do the necessary manual work and understand the system behavior to make it happen. And with the Manual testers can monitor for better result and AI can suggest us the test scenarios and we can select which to run and which does not add value to our process

Starting AI testing requires clear goals, thorough planning, tool selection, and execution. Define scope, objectives, and criteria, choose relevant tools, conduct tests, monitor data, and assess outcomes for a successful AI testing process.

To start with AI testing the language models or the backbone of the Intelligence should be first fed with the domain knowledge. The data present in internet tend to have a publisher bias and this should not be the input for the model to make sure the functionality and preferences of the features it is supposed to test is not truly dependent on those.

A checklist of what we want AI tools to achieve may be very useful when starting AI testing. This can help determine what AI testing tools we use in the first place as well! Also, this way as we can take some more accountability as AI doesn't take accountability as pointed out in the 4th point here. Furthermore, once AI testing occurs we can check if it is in fact doing what we asked it to do, and maybe even check if it is done within ethical and legal means as well

Here are some of the aspects I'm currently utilizing: - AI has the capability to transform unformatted test cases into the desired format. - AI can rapidly generate test cases once the requirements are input. - AI-based computer vision tools enable visual testing. - AI can aid in writing automation code and identifying the appropriate locator strategy for next-gen UIs. This, when developed further, can lead to self-healing tests. - AI can swiftly generate meaningful test data.

Navigating the intricate seas of mobile and web application testing demands a diligent focus on quality and user experience. While essential, this process often entwines us in a web of time, complexity, and cost, especially when sailing through varied platforms and user scenarios. Here’s a beacon: integrate AI into your testing journey. Select tools that not only automate but also learn and adapt, optimizing each testing cycle. Ensure these tools align with your existing frameworks, crafting a streamlined, intelligent, and evolving testing environment, and steering your application towards unerring functionality and user delight.

A inteligência artificial (IA) pode ser usada para aprimorar os testes de aplicativos web e mobile de várias maneiras: 1. Testes de Automa??o 2. Testes de Desempenho 3. Testes de Usabilidade 4. Testes de Seguran?a 5. Testes de Regress?o 6. Gera??o de Dados de Teste 7. Análise de Logs e Relatórios 8. Testes de Experiência do Usuário (UX) 9. Testes de Localiza??o e Internacionaliza??o Em resumo, a IA pode otimizar os processos de teste, tornando-os mais eficientes, precisos e abrangentes, melhorando a qualidade dos aplicativos web e mobile.

To add to the considerations the security of data and applications, costs involved in building such models and correctness of the models and tests done and the agreement from the stakeholders on believing such a new trends would be few points

It’s a great idea to use AI to help perform unusual tasks, for example, writing specific scripts for CI/CD in programming languages that don’t make much sense to study specifically. And AI is incredibly efficient at performing routine work like code reviews. Another unusual way to use AI is to review test cases for grammar and redundancy. Take note)