Analyzing the Impact of AI and ML on Software Testing

Analyzing the Impact of AI and ML on Software Testing

Since the earliest days of its inception, the software testing industry has continually evolved and incorporated newer technologies to produce quality software. According to the 2017 State of Testing Survey, 62% of respondents believed that fully automated testing would be a reality within the next few years. This belief is also shared by tech giants such as Google, Facebook, Microsoft, and Amazon who have now spent billions of dollars on machine learning and AI initiatives.

AI and machine learning’s potential for taking software testing to the next level was highlighted by the 2016-17 World Quality Report that pointed to machine learning intelligence as the most powerful solution for overcoming QA and testing challenges. However, the obvious question here is how AI and ML will go about enhancing the testing process.

Quality Assurance in software testing has always followed a straightforward pattern. So long as testers knew how the system would behave in certain cases, it was relatively simple to carry out necessary tests to determine how well the software reacted in accordance to set expectations. A match would indicate that the test was cleared, whereas a mismatch pointed to undetected bugs and errors that needed to be fixed – restarting the testing cycle all over again.

In such cases, testers would have to go through the entire checklist again to determine whether all the users’ actions and steps were covered and resolved. However, the consumers of today have increasing demands, which software testers are unable to meet, using traditional testing methods. The main problem lies with the immense amounts of data that needs to be analyzed in a short period. This fact alone calls for a new and effective solution – one that’s powered by predictive analytics, artificial intelligence, and machine learning.

Entering the New Age of Software Testing with Artificial Intelligence

The digital transformation within software testing has seen developers slowly shift towards automation – and by extension – Artificial Intelligence. It involves a change from the manual testing carried out by humans to coding and execution carried out by smart machines. The growing intelligence of these machines is a worrying concern for many software designers who fear being soon replaced.

Essentially, artificial intelligence enables computers to carry out tasks that, until recently, would have only been possible with the human intellect. It equips machines with the ability to process information coming from specific environments, react to changes, and come up with viable solutions. It also makes it possible to feed a computer with enormous amounts of data and watch them identify patterns and produce meaningful inferences related to input and output pairs.

Thanks to recent technological innovations, machines are able to learn much faster than humans, analyze large sums of data, use that information to create connections, and form patterns without the help of stipulated algorithms. Software testing has never been as impactful as it is today.

Through the help of Application Programming Interfaces (APIs), applications in the current technological landscape interact with each another while increasing in complexity in both linear and non-linear ways. Let’s take a look at a few ways artificial intelligence is expected to impact software testing.

Artificial Intelligence will Assist Organizations in Identifying Complex Areas

Software testing is a core function of the software development process. Usually, PHP and software testers are unable to carry out thorough application testing due to a scarcity of time and resources. It is an unfortunate – and often overlooked problem – considering the effectiveness of software development in identifying key areas that require more focus and attention from the organization.

Artificial Intelligence is the most viable option for PHP developers in pursuit of quick deployments with inadequate infrastructures. Since the significant chunk of software testing involves repetitive performance checks, AI can save considerable time and effort by automating the entire process. Also, businesses can rely on AI to identify the problems and application processes for the purposes of prompt testing.

deep learning algorithms

Artificial Intelligence will Result in Improved Accuracy

Traditional software testing still relies on the human intellect for data and source analysis but despite that, even the most seasoned quality assurance engineers make mistakes. Due to extensive data, it’s normal for software testers to lose focus and overlook certain defects and before stakeholders catch wind of any problems, customers already begin to point out bugs and errors on online forums. Such situations end up damaging the brand´s reputation as well as the product’s positioning in the market.

Situations such as these further highlight the importance of incorporating AI and ML in software testing. They enable systems to understand source analysis techniques and apply that knowledge in the future, enabling AI testers to come up with increasingly accurate results. The benefits of AI technology for data analysis extends to lowering the probability of human error and shortening the time it takes to run tests and identify defects. Consequently, the QA team isn’t overburdened with large data volumes and can focus on improving the end result.

Artificial Intelligence Allows for Better Predictive Analysis

The World Quality Report of 2016-17 mentioned earlier, also states how 64% of companies plan on integrating AI into Quality Assurance strategies to improve customer processes. Consumer demands within the IT sector hardly ever slowdown, which is forcing businesses to search for ways that predict future demand trends to get ahead of the competition.

Understanding future demands for predictive analytics is made easier with AI and ML and enable testers to carry out data analysis and provide insights into consumers’ purchasing patterns.

Artificial Intelligence Will Force Testers to Evolve and Adopt a Supervisory Role

AI testing in 2019 shows promising signs of enhancing the overall efficiency of testing processes. As the majority of test execution looks to be overtaken by AI, Quality Assurance engineers must learn new skills in order to retain their value within the company. Working alongside AI requires a diverse skill-set and firm understanding of math optimization, AI testing, business intelligence, neuro-linguistic programming, and algorithm analysis.

As it stands, there’s a lack of such qualified personnel despite the maturity and rapid growth rate of AI. Companies will, therefore, need to invest in development and training courses for their QA staff, especially regarding the following roles:

  • Artificial Intelligence Test Experts: Apart from basic testing skills, QA members are expected to create machine learning algorithms, work on NLP paradigms, and comprehend complex math models.
  • Data Scientists: Analysts within the QA team need to be able to utilize statistics, filter data, and present appropriate models using predictive analysis for effective AI-based Quality Assurance Strategies.
  • Artificial Intelligence Quality Assurance Strategists: This team is responsible for handling the AI role within business processes. They possess a deep understanding of robotics techniques, math optimization, data flows, and continually develop newer and effective ways of implementing AI Quality Assurance practices across the entire business lifecycle.

Judging from the current trends, it’s highly likely that QA engineers will turn into test automation teams. They will adopt a supervisory role geared towards teaching AI on how to execute a wide range of tests. As of today, many have stated AI to have the potential to occupy the majority of the repetitive testing space. The remainder of which will be controlled by humans to test various outcomes.

Despite the change, test managers are still responsible for workflow modeling, environmental setup, and tooling. While AI might prove to be an extremely smart assistant for running repetitive tasks, the presence of QA engineers is necessary to consider test plans, control QA strategies and objectives, and monitor the overall progress.

The need for self-development is crucial for certain developers who risk losing their positions to AI that can perform a better job with the right data. It’s no longer a question of ‘if’ but ‘when’ testers will have to be laid off to clear the way for automated testing. The more developers and testers understand the changing technological landscape, the better it will be for their careers.

Software testing is a prerequisite for smart and productive software that has fewer bugs – all of which depends on automated testing. The emergence of AI in QA testing makes it possible to identify areas that need immediate improvement. Once all the tests are carried out, developers can refine the product through trial and error.

Like most other technologies, AI also, has its pros and cons. It cannot completely eradicate the presence of bugs and error. However, one of its most significant advantages lies in its ability to provide instant feedback – something human testers are unable to achieve in the face of voluminous data. AI also assists developers in generating and optimizing test cases, enhancing UI testing, and prioritizing testing and automation.

ai machine learning

The Merits of Machine Learning in Software Testing

Though touted as next-gen technology, machine learning has been around since a very long time ago. The term “machine learning” refers to a computer’s or software’s ability to “learn” over time. It has existed for nearly as long as computers, however, the machine learning of today is vastly different.

In the not-so-distant past, machine learning was driven primarily by trends, patterns, algorithms, and formulae which were created by programmers and fed into software or machines. This didn’t make the machines “smart” since they couldn’t truly analyze data or learn on their own. Modern-day machine learning is significantly different and much more advanced. It’s autonomous and is accredited with giving birth to modern Artificial Intelligence.

Computations, algorithms, and programming remain a part of Machine Learning, but unlike the past, new Machine Learning processes enable the platform to evolve and change over time. This is because the system is powered by cloud platforms or Big Data that contain vast amounts of information waiting to be analyzed.

A recent survey carried out by the Accenture Institute for High Performance revealed how 40% of all the surveyed companies admitted to using Machine Learning to improve marketing performance and sales. This also includes cross-channel marketing and market segmentation.

Tech giants such as Google, Microsoft, Facebook, and Amazon make use of machine learning applications every day. The technology goes through several datasets including users’ personal information, purchasing habits, search preferences, and what they’re looking at to determine and deliver appropriate ads and content. In Facebook’s case, Machine Learning offers insights into how users interact with the community as a whole.

Machine Learning’s ability to identify predictive patterns is what makes it so valuable for software testing. It transforms the testing process via an automated approach that primarily focuses on back-end processes. The following are a few ways Machine Learning enhances QA in software testing:

  • User Interface Testing: The use of Machine Learning bots is a helpful and welcome addition for improving the end-user experience. The majority of modern-day applications follow a predictable pattern in relation to the interface, design, and functionality. For instance, you can easily locate a product filter, payment window, and shopping cart in an online store. Bots can be developed for a specific software area to carry out a higher number of test cases than what would otherwise be impossible with regression testing. Using visual validation tools to carry out image-based testing is a pattern that’s familiar with Machine Learning. Quality Assurance engineers can create machine learning tests that can quickly identify visual defects present within the software.
  • Strategy Orientation: Usually, QA engineers have to carry out lengthy tests due to minor complications within the code. Machine Learning tools make life easier for testers by determining the least number of tests required to carry out code modifications. It also identifies current test coverage and vulnerable software areas.
  • Application Programming Interfaces (APIs): Machine Learning is an invaluable tool for software testers seeking to carry out check-ups of API layers. The algorithm conducts a quick analysis of all test scripts saving testers from having to make numerous API calls.

Challenges Facing AI in Software Testing

Even though the incorporation of AI in software testing is the future of QA, there are a number of challenges and hurdles preventing companies from fully leveraging its benefits. Some of these challenges are:

  • AI Integration: Currently, most businesses are failing to identify and list the business areas that stand to benefit the most from AI technologies. Additionally, many QA software testers are unable to reach out to AI technologies highlighting the need to develop the proper expertise first.
  • Lack of Qualified Staff: As mentioned previously, intelligent technologies are getting smarter each day, which demands an increase in skills and expertise on behalf of the QA testers. Although the knowledge gap will get filled sooner or later, organizations currently experimenting with intelligent QA are facing a shortage of qualified testers.
  • Big Data: Processing unstructured data demands additional financial support and human resources. Compounding the problem further is the inability of most organizations to handle heavy data analysis, which also prevents them from leveraging cognitive platforms or robotics.

Useful AI-Driven Automation Tools

While AI is definitely set to take over the testing process, it’ll still be some time before we see fully automated QA in software testing. Today, QA engineers and businesses are optimistic regarding a number of new, innovative automated testing solutions. The following are some of the more popular ones:

  • Appvance: An AI-powered tool that offers deep analysis of software through machine learning and creates “application blueprint” models using cognitive generation. The blueprints have the potential to generate multiple tests in a matter of minutes.
  • Eggplant AI: This tool relies on intelligent algorithms to predict defects, resolve problems with data correlations, and navigate software. It also enables the automation of test engines while also providing graphical analysis of test results and coverage.
  • Applitools: An AI-powered visual testing tool with cognitive vision capabilities enabling developers to accurately outline expected application designs. Applitool makes it possible to detect and report differences within the user interface. The tool is also effective for carrying out automated tests to identify numerous interface inconsistencies in short periods.
  • Testsigma: An AI-driven tool used for continuous automated testing. It identifies relevant test cases for test runs and prevents unforeseen test failures. Testsigma also uses a natural language testing process to write auto tests.
  • ai: It identifies the elements and screens of software and drives the app to carry out test cases. The tool effectively adjusts to changes in elements and identifies pieces requiring manual corrections.

Conclusion

Growing consumer demands are forcing testers to analyze large quantities of data in short spans of time. AI and Machine Learning tools enable testers to better understand customers’ needs, react faster to shifting market trends and offer solutions to overcome common obstacles. However, all of this depends on having a qualified QA team.

deep machine learning

Fortunately for you, EX² Outcoding is a nearshore software development company that also provides Quality Assurance in Software Testing services. They make use of the latest technological innovations to ensure your software is error-free and lives up to the users’ expectations. Home to some of the most skilled software developers and QA experts, EX² Outcoding offers quality IT solutions at affordable rates. Contact us today for more information.

2019-05-02T17:26:56+00:00