With advancements in AI, particularly in machine learning and natural language processing, testing tools have become more sophisticated. AI-driven tools can now learn from previous tests, predict potential defects, and adapt to new testing environments with minimal human intervention. Typemock has been at the forefront of this evolution, continuously innovating to incorporate AI into its testing solutions.

RELATED: Addressing AI bias in AI-driven software testing

Typemock’s AI Enhancements

Typemock has developed AI-driven tools that significantly enhance efficiency, accuracy, and test coverage. By leveraging machine learning algorithms, these tools can automatically generate test cases, optimize testing processes, and identify potential issues before they become critical problems. This not only saves time but also ensures a higher level of software quality.

I believe AI in testing is not just about automation; it’s about intelligent automation. We harness the power of AI to enhance, not replace, the expertise of unit testers. 

Difference Between Automated Testing and AI-Driven Testing

Automated testing involves tools that execute pre-written test scripts automatically without human intervention during the test execution phase. These tools are designed to perform repetitive tasks, check for expected outcomes, and report any deviations. Automated testing improves efficiency but relies on pre-written tests.

AI-driven testing, on the other hand, involves the use of AI technologies to both create and execute tests. AI can analyze code, learn from previous test cases, generate new test scenarios, and adapt to changes in the application. This approach not only automates the execution but also the creation and optimization of tests, making the process more dynamic and intelligent.

While AI has the capability to generate numerous tests, many of these can be duplicates or unnecessary. With the right tooling, AI-driven testing tools can create only the essential tests and execute only those that need to be run. The danger of indiscriminately generating and running tests lies in the potential to create many redundant tests, which can waste time and resources. Typemock’s AI tools are designed to optimize test generation, ensuring efficiency and relevance in the testing process.

While traditional automated testing tools run predefined tests, AI-driven testing tools go a step further by authoring those tests, continuously learning and adapting to provide more comprehensive and effective testing.

Addressing AI Bias in Testing

AI bias occurs when an AI system produces prejudiced results due to erroneous assumptions in the machine learning process. This can lead to unfair and inaccurate testing outcomes, which is a significant concern in software development. 

To ensure that AI-driven testing tools generate accurate and relevant tests, it is essential to utilize the right tools that can detect and mitigate bias:

• Code Coverage Analysis: Use code coverage tools to verify that AI-generated tests cover all necessary parts of the codebase. This helps identify any areas that may be under-tested or over-tested due to bias.
• Bias Detection Tools: Implement specialized tools designed to detect bias in AI models. These tools can analyze the patterns in test generation and identify any biases that could lead to the creation of incorrect tests.
• Feedback and Monitoring Systems: Establish systems that allow continuous monitoring and feedback on the AI’s performance in generating tests. This helps in early detection of any biased behavior.

Ensuring that the tests generated by AI are effective and accurate is crucial. Here are methods to validate the AI-generated tests:

• Test Validation Frameworks: Use frameworks that can automatically validate the AI-generated tests against known correct outcomes. These frameworks help ensure that the tests are not only syntactically correct but also logically valid.
• Error Injection Testing: Introduce controlled errors into the system and verify that the AI-generated tests can detect these errors. This helps ensure the robustness and accuracy of the tests.
• Manual Spot Checks: Conduct random spot checks on a subset of the AI-generated tests to manually verify their accuracy and relevance. This helps catch any potential issues that automated tools might miss.

How Can Humans Review Thousands of Tests They Didn’t Write?

Reviewing a large number of AI-generated tests can be daunting for human testers, making it feel similar to working with legacy code. Here are strategies to manage this process:

• Clustering and Prioritization: Use AI tools to cluster similar tests together and prioritize them based on risk or importance. This helps testers focus on the most critical tests first, making the review process more manageable.
• Automated Review Tools: Leverage automated review tools that can scan AI-generated tests for common errors or anomalies. These tools can flag potential issues for human review, reducing the workload on testers.
• Collaborative Review Platforms: Implement collaborative platforms where multiple testers can work together to review and validate AI-generated tests. This distributed approach can make the task more manageable and ensure thorough coverage.
• Interactive Dashboards: Use interactive dashboards that provide insights and summaries of the AI-generated tests. These dashboards can highlight areas that require attention and allow testers to quickly navigate through the tests.

By employing these tools and strategies, your team can ensure that AI-driven test generation remains accurate and relevant, while also making the review process manageable for human testers. This approach helps maintain high standards of quality and efficiency in the testing process.

Ensuring Quality in AI-Driven Tests

Some best practices for high-quality AI testing include:

• Use Advanced Tools: Leverage tools like code coverage analysis and AI to identify and eliminate duplicate or unnecessary tests. This helps create a more efficient and effective testing process.
• Human-AI Collaboration: Foster an environment where human testers and AI tools work together, leveraging each other’s strengths.
• Robust Security Measures: Implement strict security protocols to protect sensitive data, especially when using AI tools.
• Bias Monitoring and Mitigation: Regularly check for and address any biases in AI outputs to ensure fair testing results.

The key to high-quality AI-driven testing is not just in the technology, but in how we integrate it with human expertise and ethical practices.

The technology behind AI-driven testing is designed to shorten the time from idea to reality. This rapid development cycle allows for quicker innovation and deployment of software solutions.

The future will see self-healing tests and self-healing code. Self-healing tests can automatically detect and correct issues in test scripts, ensuring continuous and uninterrupted testing. Similarly, self-healing code can identify and fix bugs in real-time, reducing downtime and improving software reliability.

Increasing Complexity of Software

As we manage to simplify the process of creating code, it paradoxically leads to the development of more complex software. This increasing complexity requires new paradigms and tools, as current ones will not be sufficient. For example, the algorithms used in new software, particularly AI algorithms, might not be fully understood even by their developers. This will necessitate innovative approaches to testing and fixing software.

This growing complexity will necessitate the development of new tools and methodologies to test and understand AI-driven applications. Ensuring these complex systems run as expected will be a significant focus of future testing innovations.

To address security and privacy concerns, future AI testing tools will increasingly run locally rather than relying on cloud-based solutions. This approach ensures that sensitive data and proprietary code remain secure and within the control of the organization, while still leveraging the powerful capabilities of AI.

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This latest version includes support for .NET Core and .NET Framework. According to the company, this will help support companies working with both new and legacy code in their environment. 

Isolator 9.1 also integrated with the latest version of Visual Studio 2022, which allows developers to do unit tests on their code without having to modify their existing tests. 

It also provides an automatic smart runner, which runs only the required tests after a change is made. This feature also allows developers to select tests to rerun. 

Isolator integrates with other runners as well, such as VS Test Explorer and JetBrains Resharper, among others. 

“We are very excited to offer our new version of Isolator for dotnet core, ” said Eli Lopian, CEO and founder of Typemock. “Isolator offers our customers a powerful solution to write unit tests and fix and debug them. Our customers report a six hundred percent improvement on average in code coverage and adding dotnet core is extending that user experience. It will enable developers to create applications across multiple platforms with a clear API mocking framework.” 

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Project Jupyter is designed to create open-source software, open standards and services for interactive computing.

“This is the largest team ever to receive this award, and we are delighted that the ACM was willing to recognize that modern collaborative projects are created by large teams, and should be rewarded as such. Still, we emphasize that Jupyter is made possible by many more people than these fifteen recipients. This award honors the large group of contributors and users that has made IPython and Jupyter what they are today. The recipients are stewards of this common good, and it is our responsibility to help this broader community continue to thrive,” the Project Jupyter team wrote in a post.

SmartBear adds new API monitoring capabilities in AlertSite
SmartBear has announced new API monitoring capabilities in AlertSite, its solution for performance monitoring for the web, mobile and APIs. The new capabilities are designed to provide operations, DevOps, and development teams with end-to-end visibility into API performance, availability, and functional correctness.

Teams will now be able to instantly create API monitors, define granular and targeted alerting, trust that alerts are real with a series of validation checks, and monitor both public and private APIs, according to the company.

Google creates conversation design site to help developers create Actions for Google Assistant
Google has created a new conversation design site so that developers can create Actions for Google Assistant following the same principles that the team at Google uses. The company’s goals are to help developers craft conversations that are natural and intuitive for users, and allow them to scale conversations across all devices to help users no matter where they are located.

It will also provide developers with tips on how to gather requirements, create system and user personas, write sample dialogs, draw high level flows, test and iterate, design for ways that conversations can deviate from normal, and ensure features work as a voice only and multimodal interaction.

Typemock releases Isolator++ for Linux
Typemock has released Isolator++ for Linux, which will allow developers to easily unit test code on Linux. Previously, Typemock had been used by developers to unit test .NET and C/C++ code on Windows.

Isolator++ for Linux allows developers to mock fields, members, and concrete classes, in additions to non-virtual, private, and static methods without having to use templates or redefined classes. It also provides a mocking framework that supports testing legacy code.

“Supporting Linux was high on our customers’ wish list and we’re delighted to release this new version to answer their need,” commented Eli Lopian, Typemock’s founder and CEO. “This release will allow organizations that develop on Linux, Windows or both Operating Systems, to benefit from a single mocking framework, while finding and rapidly fixing bugs.”

The UWP Community Toolkit is renamed to the Windows Community Toolkit
Microsoft has announced that the UWP Community Toolkit will be renamed to the Windows Community Toolkit in the next major release. The toolkit was initially released over a year and a half ago and has since grown from an initial 26 features to over 100.

“Enabling more developers is what the toolkit is all about, so starting with the next Windows Community Toolkit release, we are setting a goal to enable more Windows developers working on Windows 10 experiences to take advantage of toolkit components where possible. Therefore, the new name is reflective of this increased focus and more inclusive of all Windows developers,” the team wrote in a post.

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Visual Studio 2017 is the newest version of Microsoft’s popular development environment. Typemock’s latest release showcases its continuous product innovation and fast response to changes in the development environment. Typemock has long supported Visual Studio, and the latest release continues this tradition and allows developers to use multiple versions together to test new or legacy code and not be restricted within their team.

Visual Basic.NET developers often create critical business applications. However, they had lacked the ability to properly unit test their existing legacy code, leading to more bugs and other defects.  Typemock now offers Visual Basic.NET developers its SmartRunner, Coverage and Suggest features, that were previously available only to C# developers. Visual Basic.NET is commonly used to develop Windows Forms and other business applications, it is also the sixth most popular programming language, according to the June 2017 TIOBE Programming Community Index.

“Businesses rely on unit testing to ensure that their code is good, does what it is supposed to do, and has fewer defects,” noted Eli Lopian, CEO of Typemock. “It is an essential part of agile and DevOps processes, and ensures that quality code can get released to production. Relying on outdated software and not testing existing legacy code is a recipe for disaster. Therefore we are proud to constantly offer the ability to test more code on the latest environments, supporting whatever tool your team is using.”

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“There was a technology gap preventing effective unit testing of C/C++ code, and Isolator++ filled that gap,” said Eli Lopian, Typemock’s CEO. “Isolator++ provides the ability to fake out any C/C++ code, so the developer can easily test it. This includes non-virtual method, static methods and private fields & methods, all bundled together in an API that conforms to the AAA best practice structure of unit testing.”

According to Typemock, C/C++ is often thought of as a legacy language. This has prevented developers from using it for agile development solutions and realizing the importance of unit testing. However, C and C++ are one of the more popular programming languages, according to the Tiobe Index, and it’s used in embedded devices and in everyday software solutions. Because of this, Typemock said it is essential to test “brownfield” code, as well as new code.

Isolator++ allows users to write tests with minimal lines of code, test any C/C++ code, and test legacy code without having to change it. It also allows users to become more agile with APIs designed to make tests more concise, resistant and easy to understand.

“Many established enterprises have software that was written in C/C++,” said Lopian. “C++ has been around for over 40 years, with standards constantly updating. Mission-critical code use C++. The rise of embedded devices and the Internet of Things further push the importance of quality C++ code. We see large companies in highly-regulated industries that cannot code fast enough, that cannot be agile enough; who are not confident to make any change in their code and innovate their product.”

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Typemock has focused on making companies and developers more agile since 2005, and over the past few years, it has updated its Isolator tool to help companies automate their tests and prevent code decay.

(Related: The key to software testing is automation)

To keep up with other agile solutions, Typemock created the artificial intelligence bot Typemock Suggest. The company said it is able to suggest code that covers the parts and logic of any pre-existing code that does not have implement unit testing.

Suggest provides a template and offers a test with setup, isolation, mocking and verifications, delivering maintainable and readable tests that .NET developers can add to their own suite of tests.

“Typemock Suggest takes legacy code and it runs it through the special bot—the special AI—and it suggests a few unit tests for the developers,” said Eli Lopian, founder and CEO of Typemock. “It knows what’s happening in the code, it can analyze it and run it in different permutations and mutations.”

Lopian said that for many developers, they would love to have unit testing, but they can’t be bothered having to sit and write all the tests. He compared this to how everyone wants to be fit, but no one wants to go to the gym. In this case, developers want to be agile and change their code, but no one wants to do unit tests.

“Everybody wants to be fit, but the price you have to pay is you have to go to the gym and work out—you have to write these unit tests which aren’t actually the code that’s being used, [and] it’s not really productive,” said Lopian.

Within the Isolator tool, the isolation/mocking component allows developers to isolate their code into smaller pieces from a bigger system, letting them more easily write the tests.

There is also a smart runner component that is integrated with Visual Studio. Lopian said that this allows developers to run tests automatically or manually, and it has incremental running, which means it knows what test needs to be run so a developer doesn’t have to run tests on a whole suite.

The new version of Isolator also includes hundreds of files, upgrades, new integrations, and new isolation APIs.

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Using “discoverable” APIs and an “intelligent completion tool” called Intellitest, which generates test setup code automatically, Isolator 2010 isolates code without any complex configuration and assists with unit test writing.

“Unit testing is very fragmented,” said Typemock CEO Eli Lopian, “and this is lifting the barrier of unit testing,” ultimately reducing time to market and developing costs. “There has also been a paradigm shift from helping developers design for testability to helping them design for productivity.”

Other features of Isolator 2010 include an IDE, along with debugging integration with support for SharePoint 2010 and all versions of Visual Studio and .NET. Isolator 2010’s server edition also allows for unit testing in Microsoft’s Team Foundation Server or any build server.

Pricing for Isolator 2010 starts at US$799 per perpetual license with a $150 option for 12 months of maintenance. The server edition is $2,499 for up to five virtual machines.

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