5 best AI tools for software QA (2024 edition)

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AI technology keeps improving and evolving at exponential rates, and as a result, more and more software and tools are being built and shipped from the lower barrier to entry.

However, easier production doesn’t mean better products. With more use and integration of AI in apps, it’s also increased the complexity and unpredictability of AI model outputs. This means testing and evaluation of AI driven features and products has become even more pressing.

This is why software testing tools specifically for quality assurance (QA) have become an important component of software development so we can ensure consistent performance from AI models and identify edge cases before they become more problematic.

Why QA is Crucial for AI Products

Ignoring QA when developing AI products can be a very high risk game. Without regularly checking software with rigorous tests it can lead to biased outputs, subpar user experiences, and security vulnerabilities which can all be early indicators of product failure. If that ever happens it can be crippling for a company’s reputation.

AI models and software in general are constantly iterating, which means we need to look at QA as a continuous process of testing and monitoring if we want to quickly adapt to new data and any changes. Ongoing evaluation is the only way teams can maintain performance and reliability with their AI products.

For these particular circumstances, our open source AI analytics platform Phospho is quickly becoming an essential tool for AI quality assurance.

In this article we’ll be going over its specialised features and capabilities which are specifically tailored for AI product development, as well as some other popular QA tools. **

Overview of the 5 Best AI Tools for Software QA in 2024

As the AI landscape evolves at this rate, we need our QA tools to keep up pace with the demands of AI product development.

Traditional QA methods often fall short because of the difficulty in capturing the nuances of user inputs and AI responses, which necessitates the use of specific tools.

Here, we’ll explore the 5 best tools for AI software QA in 2024 with detailed breakdowns.

Detailed Breakdown of Each Tool:

To get a quick understanding of each tool we need to look at its best use case, key features, and any limitations so we can assess which is best for us.

1) Phospho.ai

Phospho is an open source text analytics platform specifically designed to refine and improve LLM apps through real-time insights and continuous evaluation.

Value Proposition

You can use Phospho to log every user interaction with your LLM and perform real-time analytics. What better data than leveraging in-app feedback and conversations for maximum relevance and volume? With Phospho’s robust analytics features and visualisations teams can use this real-time data to find product market fit faster and future proof themselves to any market changes.

To understand how using Phospho with the product process matrix can help you reach product market fit faster, read our previous article here.

Key Features

• Real-time monitoring of user interactions lets you track and log user inputs to identify issues or trends and continuously fine-tune the performance of your LLM app.
• Automated insights extraction and KPI detection so you can create your own KPIs and custom criteria to ‘flag’ for, and you can label if it was a successful or unsuccessful interaction.
• A/B test different versions of your LLM app to see which ones perform better with your users.
• Continuous evaluation and iteration support. You can use our automatic evaluation pipeline that runs continuously to keep improving your AI model’s performance.

Use Case

Phospho’s AI native approach makes it the most ideal use case for startups and companies building LLM integrated products. Particularly those that could leverage AI analytics on user interactions to iterate more effectively and quickly towards product market fit.

Limitation

Phsopho is an open source tool so may require a little technical know-how to set up and integrate for its full capacity using our API. However, to give Phospho a try with no code you can simply just import your own test data like a CSV or Excel file and test out its features this way.

To see how, you can try for free by signing up here.

2) Applitools

Applitools is a visual testing and monitoring platform that uses AI to catch visual bugs across different devices. It uses a visual AI engine that mimics human eyes and analyses screens to spot inconsistencies we could easily overlook if doing manually.

Value Proposition

You can use Applitools to automate the process of visual testing and spotting any inconsistencies in your software’s UI. With this tool you can maintain a consistent user experience without having to do any of it manually.

Key Features

• Automates the detection of UI inconsistencies that can be overlooked manually
• Cross browser testing - ensure it’s visually the same across different browsers
• Integrates directly into CI/CD pipelines for continuous visual testing

Use Case

Given Applitools’ focus on visual testing it’s best used for front end heavy software such as e-commerce platforms or design-centric apps that need high focus on UI/UX consistency for their user experience.

Limitation

It’s greatest strength might also be it’s limitation to some teams. With such a high focus on visual testing it’s not a suitable tool for all round QA if you needed testing for backend functionality such as code logic or system performance.

3) TestRigor

TestRigor is a no code platform for QA where you can write your tests in plain english. This obviously opens the door to people with minimal tech background.

Value Proposition

You can use TestRigor’s platform to simplify testing because it doesn’t require any coding. Their approach democratises the QA process and makes it accessible to all team members and stakeholders to contribute.

Key Features

• It’s codeless so you can write tests in plain english
• End to end testing for web, mobile and browsers
• Easily integrates with popular CI/CD tools and issue tracking systems e.g Jira

Use Case

The no code approach makes TestRigor most suitable for teams with little or no no coding expertise who need to start implementing automated tests and QA quickly without much friction. It’s obviously an ideal tool if non technical stakeholders such as Business Analysts also need to contribute to the QA process.

Limitation

The simplified and accessible approach might be great for some but doesn’t suit teams who like more control and customisation over their test scripts. For example, more complex testing such as custom logic and specific test flows might be difficult to implement with TestRigor’s lack of flexibility there.

4) Mabl

Mabl is a cloud based end to end automated software testing platform for web apps. That’s a bit of a mouthful but Mabl’s AI powered capabilities are great for streamlining your QA when developing products that need regular updates.

Value Proposition

You can use Mabl’s machine learning algorithms to automate test creation and execution to cut down on your maintenance efforts. It even has a unique feature that automatically adapts tests based on any UI changes so you don’t need to invest as much time into constantly updating your tests.

Key Features

• ‘Self healing’ tests - which basically adapt to any UI changes so minimal manual intervention is needed to update tests
• Mabl’s intelligent wait can adjust test execution based on real-time app behaviour to help with reliability and save time
• Detailed analytics on test results to help identify and fix issues faster

Use Case

The value add from AI and ML makes Mabl best suitedif you need to ship regular updates or iterations. When needing to ship and deploy so often, tools like Mabl are ideal for teams looking to streamline and maximise their QA through automated testing because you would need far less time investment or overhead to manage your QA.

Limitation

Utilising AI and ML for your QA might be great for streamlining your process, but there is potential for false positives when relying on ‘self healing tests’ and automation as it may not always accurately capture complex changes. Therefore, software that really requires precision and accuracy, you might need to add a layer of extra verification and validation in the process which can counter balance the time saving benefits Mabl offers.

5) PractiTest

PractiTest is a comprehensive ‘all in one’ test management tool that provides more control and analytics from your testing efforts. It’s robust and extensive for very thorough QA.

Value Proposition

You can use PractiTest to centralise your QA tests, processes, analytics, and teams into a single source of truth. This helps foster more collaboration and eliminate any silos which is useful for companies with large scale products and multiple teams.

Key Features

• End to end test management means you centralise all the tests and defects, facilitating clear communication and data driven decisions
• Test value score is a unique feature that assesses test case impact, allowing teams to focus on high value test and distribute resources more effectively
• Customisable dashboards for tailored real time insights
• Robust integrations with tools and CI/CD pipelines - very versatile for more complex environments

Use Case

The centralised approach to QA you can achieve with PractiTest make it very suitable for large scale companies with multiple teams that need different testing requirements and tools, but still want to connect everything together for a holistic view. It’s built for robust testing and analytics whilst providing collaboration and visibility across big teams and different stakeholders.

Limitation

If you’re not a large scale company, the complexity of its features can be overwhelming and not worth the trade off if all you need is a simple pick up and go option. In these instances tools like Mabl or TestRigor would be more suitable for smaller teams with straightforward testing needs.

Conclusion: Which tool for you?

Proper QA is only going to be more important with the increasing speed at which we can build and ship products.

As we’ve seen in this article, there’s no one size fits all solution to QA because every team’s use case and specific needs vary. This is the most important factor to take into consideration when choosing a QA tool for you:

If you’re building a software product with an LLM integration, Phospho is the standout option for your QA with its unique features to better fine tune AI model performance.

However, for small teams looking to adopt QA with more automation then Mabl and its self adapting tests presents a good option to rely less on overhead and lean into streamlined processes. Alternatively, if you’re small scale and looking to get started quickly, no code tools like TestRigor for quickly written tests or Applitools if you simply want to test your frontend are viable options.

Lastly, for more comprehensive tools with a learning curve, teams can look at PractiTest for its intention to provide robust testing for large scale operations.