Author Archives: Ivan Pinto

Ivan Pinto
Ivan Pinto is an Associate VP of Delivery at Robosoft Technologies, leading application development, engineering, and QA teams. With expertise in web technologies (React, Angular, Node.js), mobile platforms (Android, iOS), and CTV & OTT streaming solutions (Samsung, LG, Roku, etc.), he specializes in delivering high-impact software services for global clients. A transformational technical leader and digital strategy expert, Ivan drives enterprise-wide digital and cloud transformations, aligning business objectives with technical execution. Committed to maximizing performance, quality, and ROI, he fosters team growth and continuous innovation to deliver scalable and future-ready technology solutions.
AI & Automation

AI in software testing: driving software QA forward

Artificial Intelligence in software testing blog by Robosoft Technologies feature image

AI in software testing transforms how software is planned, built, and maintained. It simplifies testing workflows, significantly enhancing productivity and efficiency across teams:

  • For QA teams: automate regression tests, focus on exploratory work, and avoid script maintenance.
  • For developers: accelerate test automation with a minimal learning curve.
  • For business analysts & PMs: quickly create and run tests without coding or extensive training.

This blog explores how AI is helping Quality Assurance (QA) by speeding up test case generation, improving regression testing accuracy, and enhancing predictive test analysis.

→ More about how AI is transforming the Software Development Lifecycle (SDLC).

AI-driven methods in automation testing

AI in software testing brings speed, accuracy, and adaptability to an often-complex process. Let us look at a few AI-driven methods that help teams deliver value:

  • Self-healing automation

Frequent code changes can break traditional test scripts, draining time and resources. With self-healing automation, AI instantly updates these scripts, reducing manual intervention and ensuring tests remain accurate as the application evolves.

  • Intelligent regression testing

Validating old features after introducing new ones can be time-consuming. AI automates regression tests based on code changes, accelerating test cycles and freeing teams to focus on strategic, creative problem-solving.

  • Defect analysis and scheduling

Machine learning identifies high-risk areas in the code and prioritizes critical test cases. This approach ensures testing efforts go where they matter most while intelligent scheduling optimizes resources for maximum efficiency.

Challenges and considerations in integrating AI in software testing

With AI-driven programming assistants like GitHub Copilot, Amazon CodeWhisperer, and Tabnine, teams can automate repetitive tasks, reduce human errors, and improve software quality while maintaining rapid release cycles. However, AI is not a silver bullet—it enhances workflows but still requires thoughtful integration into development processes. AI complements human expertise but isn’t a standalone solution. It excels in automation and pattern recognition but requires human oversight for context and judgment.

1.     Data dependency

AI-driven testing thrives on vast, high-quality datasets. Poor training data may result in unreliable test recommendations. However, sourcing, curating, and maintaining these datasets is time-intensive, adding complexity to AI integration in software development.

2.     Demand for skilled AI developers

AI enhances efficiency, but harnessing its full potential requires expertise. Skilled AI developers are essential to fine-tune models, interpret results, and optimize AI-driven testing. As demand for AI specialists rises, organizations face challenges acquiring the right talent to drive innovation.

3.     Adapting to AI-driven workflows

Shifting from traditional testing to AI-based approaches requires flexibility. Teams accustomed to manual testing may hesitate to adopt AI tools. Training and real-world demonstrations help bridge this gap.

4.     Safeguarding data and privacy

When sensitive information is involved, security and compliance become paramount. Especially in heavily regulated industries, teams must ensure the AI tools they use protect proprietary data and meet all legal requirements.

5.     Addressing technical and resource needs

Deploying AI-driven testing at scale requires thoughtful investment. It may necessitate software upgrades or enhanced hardware capabilities. While AI adoption requires upfront investment in training and resources, its efficiency gains make it a strategic asset over time.

Key tasks that AI can automate

AI can quickly learn repetitive tasks and apply them across multiple workflows, reducing overhead and speeding up quality checks. These tasks include:

  • Identify code changes and select critical tests to run.
  • Automatically building test plans.
  • Updating test cases whenever small code changes occur.
  • Planning new test cases and execution strategies.
  • Generating test cases for specific field types.
  • Automating similar workflows after learning from one scenario.
  • Deciding which tests should run before each release.
  • Creating UI-based test cases for different components.
  • Generating load for performance and stress testing.

Below is an example of using GenAI for WCAG accessibility checks. It generates multiple scenarios and elevates the overall quality of testing.

AI in software testing prompt

AI in software testing prompt response

AI in unit testing: a game-changer for developers

One of the most impactful applications of AI in software testing is automated unit test generation. Writing unit tests is often deprioritized due to time constraints yet skipping them can introduce hidden defects. AI-driven programming assistants help by automatically generating comprehensive test cases, ensuring better test coverage without additional developer effort.

Tools such as TestGrade and LambdaTest are also expanding AI’s role in integration testing. By identifying potential issues before deployment, AI-powered automation reduces regression bugs and enhances overall software reliability.

AI in regression testing

Regression testing validates whether new code has unintentionally broken existing functionality, an essential safeguard as frequent releases become the norm. For CTOs managing large portfolios, this process often balloons in cost and effort with traditional, manual methods.

By integrating AI, enterprises dramatically cut the overhead of traditional regression testing. AI tools automatically identify test scenarios, generate scripts, and adapt to code changes, minimizing manual maintenance. Predictive analytics flag high-risk areas, letting teams focus on the most critical components. As a result, testing cycles become faster and more accurate, accelerating time-to-market while reducing overall costs and risk.

Using AI agents in software testing for greater efficiency

AI is entering a new phase defined by AI assistants (reactive systems that respond to user prompts) and AI agents (proactive systems that autonomously strategize and accomplish tasks). Agents handle tasks like test case generation, test execution, and issue identification. By leveraging NLP, these agents convert simple prompts into automated scripts and adapt to changes with self-healing features, reducing manual intervention and enabling continuous feedback in CI/CD pipelines.

Their real efficiency boost comes from running tests around the clock, in parallel, and at scale—covering more scenarios faster than any human team. By analyzing past data, AI agents pinpoint high-risk areas and optimize test coverage. The result is shorter test cycles, lower costs, and more reliable software releases that keep pace with evolving user and market demands.

The future of AI in testing: what’s next?

Looking forward, AI is set to become more sophisticated in software testing. Bug detection, code refactoring, and automated debugging are areas where AI will have a greater impact. We are also seeing early capabilities in AI-assisted language migration, where code can be translated from one programming language to another—such as Ruby on Rails to Java.

However, adopting AI tools should not be a knee-jerk reaction. It’s critical to select tools that align with development environments and technology stacks while ensuring they integrate seamlessly with existing workflows. AI adoption should be a strategic decision, not a reaction to industry trends.

Also check the following articles for a deeper dive into future:

Final thoughts

The role of AI in software testing should be seen as augmentative rather than a replacement for skilled testers and developers. While AI is still evolving, its future impact on software engineering will be profound.

Integrating AI in the software development life cycle isn’t just a technological upgrade. It’s a strategic shift that accelerates release cycles, reduces costs, and sustains quality across the SDLC. From automated test creation to intelligent bug detection, AI empowers QA teams, developers, and business stakeholders alike to move faster without sacrificing precision.

If you want to enhance your QA processes or learn more about practical AI applications in development, now is the time to explore your options. Whether pilot projects or full-scale adoption, our team can help you identify the best path forward to see a real, measurable impact on software quality.

Contact us to kick start your project using AI

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AI & Automation

AI in software development: increase efficiency and drive enterprise value

AI in software development is reshaping how organizations navigate digital transformations. Yet many engineering teams, in their pursuit of agility and DevOps, find themselves bogged down by complexity, dependencies, and cognitive overload. As productivity stalls and time-to-market risks mount, AI in the software development lifecycle emerges as the critical enabler to drive enterprise value.

It takes sheer resilience to chase evasive bugs and manage the development process. Generative AI is changing this by making coding smarter and more efficient. Let’s explore how AI empowers teams to boost efficiency and gain insights once out of reach.

benefits of ai in sdlc

Impact of AI in software development

What does making an impact in software development really mean? As a developer, it is delivering maximum value to your customers while channeling your energy and innovation toward business goals. An effective environment streamlines the path to deploying high-quality software into production, preventing unnecessary complexities or delays. By removing friction and automating repetitive tasks, AI amplifies these benefits across the Software Development Life Cycle, freeing developers to focus on the value-adding work that truly drives impact. Let’s look at some of the ways AI is reshaping the SDLC.

AI in requirement gathering

Poorly managed requirements often lead to rework and cost overruns. AI-driven tools mitigate these risks by accelerating and refining the requirement-gathering process.

Tools like Jira AI Assistant seamlessly integrate with existing workflows to auto-generate user stories, maintain consistent formats, and break parent-level requirements into granular tasks. Meanwhile, GenAI uses inputs like project goals and personas to draft initial user stories, complete with acceptance criteria, desired outcomes, and dependencies.

AI in design

AI-powered design tools help us analyze and evaluate website and app design quality and usability. These tools help accelerate the design process, explore design options, and optimize UX. Design systems like Figma’s AI features can suggest component variations and styling options. Also, with AI plug-ins we can translate designs directly into code (HTML/CSS/React components) thus reducing the coding time for developers.

ai in design - ai tools enhancing software design

AI in coding

AI-powered tools like GitHub Copilot accelerate and enhance coding by offering suggestions, automating boilerplate code, and enforcing consistent standards. They free developers from repetitive work, letting them focus on complex problem-solving and innovation. By analyzing patterns from vast code repositories, these tools detect bugs early, suggest optimizations, and promote best practices. In doing so, they help maintain cleaner, well-documented code, reducing technical debt and boosting overall software quality and productivity.

AI in software development github copilot use case

Check out below podcast to discover insights from our hands-on experience with GenAI tools and how they enhance coding efficiency, optimize code quality, and streamline the development process.

Benefits of AI based coding assistants

Accelerate coding speed: suggests code snippets, functions, and even entire blocks of code based on context, significantly reducing time spent on routine coding tasks.

Reduce cognitive load: handles boilerplate code and repetitive patterns, allowing developers to focus on higher-level problem-solving and architecture.

Improve code quality: can suggest best practices and help maintain consistent code style, potentially reducing bugs and improving maintainability.

Unit test generation: helps create unit tests, potentially increasing test coverage with less manual effort.

Context-aware assistance: understands the codebase context, providing suggestions relevant to the specific project rather than generic solutions.

Multi-language support: works across numerous programming languages and frameworks, making it versatile for different development environments.

Learning tool: helps developers discover new approaches, libraries, and patterns they might not have known about, serving as an educational resource.

Documentation: assists in writing code comments and documentation, encouraging better documentation practices.

Agent mode: in recent development, the code assistant can help you build apps in fully autonomous mode. So, it can break down complex tasks into manageable steps and implement solutions across multiple files or components with least intervention from developer. This is a big step towards achieving 90% to 100% AI Assisted coding in future.

Also check the following articles on Agentic AI:

Benefits of AI based tools in SDLC

AI in software testing

AI is transforming software testing with automated test case generation, intelligent bug detection, and enhanced API validations. Tools like ChatGPT and GitHub Copilot speed up test script creation and reduce repetitive tasks, improving overall test coverage and stability. By integrating these solutions into CI/CD pipelines, teams get rapid feedback and maintain higher-quality releases with reduced manual effort.

Unit testing with GitHub Copilot

A standout use case of AI-driven testing is automated unit test generation, where Copilot suggests targeted tests for edge cases, common inputs, and potential failure modes. This proactive approach to generating code scenarios significantly cuts down on development time. As a result, teams often see a 20–25% reduction in overall testing efforts, making AI a strategic investment that boosts reliability, reduces costs, and accelerates time-to-market.

AI in Continuous Integration/Continuous Deployment (CI/CD)

AI-driven solutions in CI/CD pipelines streamline and automate build and deployment processes. By using AI-enhanced Jenkins plug-ins, teams can detect deployment failures or performance regressions in real-time and automatically roll back to a stable build. Integration with AI-based monitoring tools such as New Relic, DataDog or Splunk enables proactive remediation when abnormalities arise.

AI capabilities in SonarQube provide continuous analysis of code, identifying bugs, vulnerabilities, and code smells. Over time, SonarQube learns from developer feedback, refining its rule set and prioritizing the most critical issues and helps getting AI-generated fix suggestions.

Key highlights

  • Enterprises are increasingly leveraging AI to accelerate software delivery, enhance product quality, and unlock advanced insights.
  • AI in software development streamlines requirement-gathering, design, coding, testing, and deployment, driving agility and reducing overhead.
  • Tools like Jira AI Assistant and GitHub Copilot automate repetitive tasks, refine requirements, and accelerate coding, freeing developers to focus on complex problem-solving.
  • Automated test generation and intelligent bug detection significantly lower testing efforts, boosting reliability and cutting time-to-market.
  • AI-enabled CI/CD pipelines detect anomalies, trigger safe rollbacks, and optimize build steps, delivering faster, stable releases that enhance enterprise value.

Final thoughts

For organizations looking to integrate AI into their development and testing processes, the key is to focus on practical, measurable benefits rather than chasing the latest trends. Thoughtful implementation will ensure AI works as a force multiplier, enabling teams to build high-quality software with speed and precision.

Let’s continue the conversation. What has been your experience with AI in SDLC? Are you seeing measurable improvements in your development cycle? Drop a comment or connect with us to share insights.

Connect with us for using AI in your software product using our AI expertise

 

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