Beyond the Lines: Challenges of Automating QR Code and Barcode Scanning on Mobile Devices

Statista, the global data and business intelligence platform, reports that the number of smartphone users in U.S.A who scanned QR codes on their mobile devices in 2022 was 89 million – a 26 percent increase from 2020.

Barcodes and QR codes are the language of business in today’s digital world. They introduce a standardized process to encode information, which allows for rapid, yet accurate, identification and retrieval of data. Barcodes and QR codes have become dynamic tools with applications across industries and domains, such as retail, manufacturing, logistics, and healthcare.

Barcode and QR code scanning has become a crucial requirement for connected device testing, but automating this process can be challenging. Manual testing, on the other hand, is often necessary, but can be time-consuming and inefficient.

This blog discusses the challenges of automating barcode and QR code scanning using mobile phones and suggests solutions.

Obstacles in Automating QR Code and Barcode Scanning

Testing involves scanning a large volume of codes. Automating this phase is a challenge, especially when using mobile phones. This is primarily due to the following factors:

1. Replicating human movement to ensure stabilization while scanning

Automating the scanning process requires the replication of human-like physical movements, in order to accurately position the phone over the code for scanning. One innovative solution to address this issue, is to integrate a robotic arm, controlled by Python. This arm precisely aligns the phone’s camera with the monitor, enabling uninterrupted code scanning. Another viable approach to automating barcode and QR code scanning is to use a manual device to position the camera in line with the monitor. This method is far simpler to set up, than integrating a robotic arm, and cost effective as well.

2. Streamlining code navigation and maintenance

Expanding automation coverage requires scanning a larger number of codes. However, managing and maintaining the large volume of codes within the automation scripts, can prove to be a challenge. Web automation relies on dynamic code generation to produce codes on-demand for testing. This approach is powered by tools like Selenium, which can generate codes that are specific to the application being tested. Mobile automation, powered by Appium, can then scan these dynamically generated codes flawlessly. This synergy of approaches not only streamlines code maintenance, but also allows for comprehensive end-to-end testing on both web and mobile platforms.

3. Integrating and harmonizing diverse technologies to achieve seamless automation

Synchronizing diverse technologies is essential for successful automation. This can be achieved by integrating mobile and web automation with physical device-movement control. However, harmonizing these technologies can be a demanding task. The solution is to develop a unified test automation framework. This framework forms the backbone, coordinating the amalgamation of mobile, web, and physical movement automation components. This approach can guarantee consistency and coherence, resulting in a seamless end-to-end automation experience.

Two Technologies that can Revolutionize QR/Barcode Scanning Automation

The industry has a very optimistic outlook on the future of automation of QR code and barcode scanning on mobile devices. AI integration and cloud-based automation are two technologies that have the potential to revolutionize the way we scan these codes.

  • AI integration: AI can improve the accuracy and efficiency of QR code/barcode scanning automation. Employing AI can help intelligently recognize and process QR codes and barcodes, even if they are damaged. This can reduce the need for manual intervention, saving time and costs.
  • Cloud-based automation: Cloud-based automation can facilitate scaling up QR code and barcode scanning automation, to meet the needs of a growing business. Cloud-based automation is flexible and can be easily adapted to new requirements. Additionally, cloud-based automation can be centrally managed, which can streamline maintenance and troubleshooting.

Benefits of Automating QR Code and Barcode Scanning on Mobile Phones For Users and Businesses

  • Reduces human intervention and errors: Automated scanning can eliminate the need for manual scanning, further reducing errors and improving accuracy.
  • Streamlines testing workflows: Automated scanning can streamline testing workflows. This can save time and resources.
  • Enhances collective efficiency: Automated scanning can enhance the collective efficiency of the scanning process by reducing the time and effort required to scan QR codes/barcodes.
  • Yields reliable and accurate results: Automated scanning can yield highly reliable and accurate results by adhering to pre-defined instructions. This is especially beneficial when scanning larger QR codes and barcodes.
  • Ensures faster execution times: Real-time scanning can ensure faster execution, by eliminating the need to wait for the scan to be completed. This will help provide a seamless user experience.
  • Opens up new possibilities for mobile automation: Automated scanning opens up new possibilities for mobile automation, by enabling effortless integration of QR code/barcode scanning into mobile applications.

As technology advances, we can expect to see more innovative and efficient approaches and methods to scan QR codes/barcodes on mobile devices.

Author Details

Gowrisankar Rajendran

Gowrisankar Rajendran is a technology architect with over 18 years of extensive experience, spanning various technologies and domains. His expertise lies primarily in the realm of mobile and web-app automation. He has extensive experience in successfully delivering innovative and cutting-edge solutions to clients across diverse industries. Gowrisankar holds a bachelor’s degree in electronics and communication engineering, and a master’s degree in communication systems. He is passionate about technology and believes in continuous learning and experimentation.

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