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Enterprise mobile apps are increasingly important tools for ambitious companies that want their employees to collaborate and make decisions more effectively. How can AI (artificial intelligence) improve the testing process and quality of enterprise mobile apps?

Mobile Apps Are Crucial For Enterprise

Mobile apps are an obvious strategy for ambitious companies that want to empower their teams to work more efficiently. Most people turn to mobile apps throughout their day to communicate, discover information, book events, and solve problems.

Just as mobile apps for WhatsApp, Facebook, and Twitter are relied on for basic social functions, many employees instinctively switch to apps like Slack, Evernote, and Zoom when they want to work, communicate, and share ideas.

71% of employees already spend more than two hours a week using mobile devices to access company information, according to a report by Fliplet.

Fliplet also reports that businesses get an additional 240 hours of work from each employee who uses enterprise mobile apps. Adobe also found that ‘61% of organizations believe that if a company hasn’t deployed any enterprise mobile apps yet they’re at a competitive disadvantage’.

A range of exciting emerging technologies stands to catalyze mobile enterprise app development and compound the potential business benefits available.

Which emerging technologies are likely to catalyze mobile enterprise app growth?

  • 5G will enable lower latency for real-time hardware control.
  • Blockchain secures transactions for enhanced security levels.
  • AI offers adaptive decision-making and business insights.
  • IIoT (Industrial Internet of Things) networks sensors with software to deliver real-time analysis of
  • manufacturing processes.
  • Edge computing offers low-latency data processing.
  • Cloud computing offers scalable and cost-effective computing resources.

More than 2.6 billion smartphone users are expected by the end of 2019 – so there’s a growing incentive for ambitious companies to leverage these devices and emerging technologies by developing enterprise apps that enable better decision-making.

Enterprise Mobile Apps Need AI Automated Testing

As enterprise mobile apps become integral business tools that are connected to sensitive company data, developers will have to tackle significant security and performance issues throughout the CI/CD pipeline.

Any kind of mobile app requires regular updates. Aside from enabling useful new features, it’s important to ensure ongoing compatibility with enterprise systems and security fixes. However, it can be difficult to balance the conflicting demands for evolution and security.

Challenger banks have already discovered the benefit of Agile and DevOps software development models, which distribute security throughout the development process and guide developers to build secure architecture and testing strategies from the start.

A ‘Shift-left’ strategy that encourages developers to start testing as early in the SDLC (software development lifecycle) as possible is also helpful. By developing clean and high-quality code from the start, errors and vulnerabilities can be minimized.

However, a lack of test automation remains the biggest bottleneck for organizations that are trying to shift to DevOps and Agile development models.

How can test automation benefit the development teams?

  • Improved test coverage
  • Better control and transparency of test activities
  • Better reuse of test cases
  • Reduction of test cycle time
  • Better detection of defects
  • Reduction of test costs

However, less than a quarter of test cases (24%) use test automation. Nearly two-thirds of senior decision-makers in corporate IT functions report that ‘releases are getting very complex, often involving multiple applications with dependencies and different technologies with potentially conflicting resources’.

Artificial Intelligence and ML (machine learning) can help overcome the challenges of integrating test automation into a CI/CD pipeline by automatically testing source code changes and notifying developers when tests fail.

Test coverage can be increased

Machine learning can enable test automation software to update tests in response to code changes and adjust for unusual outliers.

Tests can be quicker

Tests for scripting, execution, and analysis can be automated. Defects can be detected and fed-back to developers more quickly. Test cases can be run in parallel.

Higher-quality testing is possible

Broken tests can be predicated, or automatically found and fixed using supervised, unsupervised, and reinforcement learning methods.

Tests can be optimized

Quality-assurance data can be used to identify appropriate test scenarios. Test orchestration can be optimized and prioritized for each release by automatically identifying failures that don’t indicate a problem in the application under test.

Businesses that invested in test automation early are reaping the biggest benefits. AI is set to transform test automation and the biggest winners are – again – likely to be those that invest early. Try BitBar’s AI Testbot for free.

Alex Napier Holland

Tech Business Writer