A large majority of software development teams think that their testing is subpar. Despite investing heavily into quality assurance, these companies still aren’t getting the results they are looking for. Now, this isn’t due to a lack of effort on their team’s part or a dearth of talent, but solely due to the technology they use. Simply put, the technology supporting software testing hasn’t been effective on its own. The industry has been underserved.
Applications and software cannot be released until it has been thoroughly and properly tested, and testing can oftentimes use significant resources considering the amount of time and human effort that is required to get the job done right. This gaping need is just starting to be filled.
Take Machine Learning (ML) for instance. We can already see its impact in various industries but it has only recently started to influence software testing in a significant way. While it is true that machine learning is still growing and evolving, the software industry is utilising it more and more, and its impact is starting to drastically change the way software testing will be conducted as the technology progresses.
Machine Learning and Software Testing
Machine learning is used synonymously and often confused with artificial intelligence (AI) but the two aren’t exactly the same thing. ML uses algorithms to make decisions and then relies on feedback from human input to update those algorithms.
A good example of this is machine vision. A machine vision software can potentially identify something as a cat when in fact it could be a dog. This outcome is corrected by a human and the set of algorithms that decide whether something is a cat or a dog gets updated depending on this feedback. Similarly, machine learning is built to make better decisions over time based on this continuing feedback from testers and users.
Machine learning has found it difficult to reach the world of End-to-End (E2E) testing because of a lack of data and feedback. Typically, E2E testing is built around human intuition and gauging what’s important to test, or what features seem risky or important. Recent software and applications are utilising product analytics data to improve and inform test automation, thus allowing for machine
learning cycles to substantially accelerate test maintenance and construction.
Predicting the Future of Software Testing
In a nutshell, the future of software testing includes faster tests, quicker results, and perhaps most importantly, tests that learn what really matters to users. The end goal of testing is to ensure that the user experience is of the highest quality and without any hitches. If at some point in the future we can teach machines what users really care about in software, we can make the testing experience for ourselves better and easier than ever before.
Conventionally, testing slows down overall development, both when it comes to speed and utility. Today, test automation and by extension, automation testing tools, are a weak link for engineering teams. Machine learning can help bolster them into a strength. Machine learning will eventually bring full autonomy to the future of software testing. By using data from current application usage and previous testing experience, smart machines will be able to build, maintain, execute, and interpret tests without the need for human input.
But the converse is also true, in that not all aspects of software development should be automated. E2E testing has a long tradition of being driven primarily by human intuition and QA technicians and the industry as a whole may not be fully comfortable handing the process over to machines. The debate on whether machines could ever replace humans at their jobs is a heated one in many industries, and industries that have resisted the rise of ML and instead, doubled down on human effort, often find themselves left behind. This is similar to the software testing industry too. Today, ML-driven test automation is in relative infancy, but it is perhaps only a few years away from taking over the industry.
Automated End-to-End Tests
Machine Learning’s main advantage in E2E testing is having the ability to leverage highly complex product analytics data to spot and anticipate user needs. ML-driven testing is in a prime position to observe every single user interaction on software, understand the common journeys that end-users walk through, and ensure that these use cases always work as intended.
If a machine is responsible for testing multiple applications, then it can learn from all of them to anticipate and predict how new changes to an application will affect the user experience. ML-driven testing can already construct better and more meaningful tests than humans because of the available data.
Compared to test automation done by humans, the tests developed by ML-driven automation are maintained and built much quicker and with far fewer costs involved. Such testing results in much faster (and higher quality) deployments and may be a boon for any VP Engineering’s budget.
Integrating Machine Learning into Software Testing
1. Risk Management and Error Prediction
Machine Learning’s integration into software testing introduces a robust mechanism for preemptive risk management. Through predictive analytics and error pattern recognition, ML algorithms can forecast potential glitches or performance bottlenecks. By analyzing historical data and detecting patterns in application behavior, the machine-learned models can predict potential issues even before the code reaches the testing phase. This foresight allows development teams to proactively address vulnerabilities, reducing last-minute troubleshooting and enhancing the overall quality assurance process.
2. Personalization and Customization in Testing Strategies
As software user expectations grow more diverse, Machine Learning enables tailored testing strategies. ML algorithms can segment and understand various user profiles, leading to personalized test cases and scenarios that closely mimic user behaviors. With this insight, the testing can better simulate real-world usage, ensuring that the application caters precisely to different user needs and preferences. Customized test scenarios offer developers a clearer perspective on how software performs under varied conditions, ensuring comprehensive testing across diverse user groups.
3. Ethical and Fair AI Implementation in Software Testing
With the increasing reliance on ML in software testing, ethical considerations are gaining significance. As the algorithms evolve and automate numerous testing processes, ensuring fairness and unbiased testing becomes crucial. Embracing responsible AI practices in software testing is necessary to prevent unintentional biases in test scenarios or outcome interpretation. Software testers, alongside ML models, need to work in unison to identify and rectify any potential biases, safeguarding ethical testing practices and promoting fairness in software development.
4. Dynamic Test Case Generation and Evolution
Machine Learning’s application in software testing extends to the generation and evolution of test cases. ML algorithms can dynamically create and modify test scenarios based on evolving user interactions and changing application functionalities. By continually analyzing user feedback and usage patterns, ML-driven systems autonomously update test cases to ensure that the software is continuously assessed for new functionalities and real-world user scenarios. This dynamic approach to test case generation significantly enhances the adaptability and coverage of testing strategies in fast-paced software development environments.
5. Augmented Collaborative Testing Ecosystems
The advent of Machine Learning in software testing fosters an environment of augmented collaboration between human testers and automated systems. ML-driven testing tools empower human testers by providing intelligent insights and suggestions for better test coverage, enabling a more efficient workflow. These tools assist in prioritizing test scenarios, identifying redundant tests, and suggesting optimal test sequences. The collaborative ecosystem between human expertise and machine-driven insights leads to more efficient and effective software testing strategies while augmenting the capabilities of the testing team.
Testers on the Way Out?
What about the people currently doing these jobs?
Quality engineers still have a serious role to play in software development. The foremost efficient technique to assure quality in software is to embed QC and QA into the planning and development of the code itself. Testing only exists because that process is imperfect.
As ML takes over the burden of E2E testing from test engineers, those engineers can use their expertise together with software engineers to create high-quality code from the bottom up. Going by recent trends, it seems most quality engineers would far prefer this too.
The Future Is Bright
ML brings forward a more effective and streamlined software testing process. It establishes a process that’s better equipped to handle the number of developments and make the needed specialized tests. Intelligent software testing would lead to the utilisation of data-based tests, accurate results, and innovative industry development.