AI based automated regression suites in end-to-end testing

Author: Nagaraj M C, Chief Quality Officer, Simplify3x Software Pvt. Ltd.

As we all know end-to-end testing involves many levels of testing from upstream UI testing to downstream database testing, each with its specific focus. In today’s world, the system architecture is designed as a collection of services and components that have complex relationships with each other. Beyond component-level testing, it's crucial to validate interdependent chains of test scenarios meticulously to bolster confidence in product delivery.

With the rising requirement for shorter delivery cycles, very effective test automation is required. Artificial intelligence is overpowering a variety of sectors, addressing both human and machine activities. This AI integration considerably improves the efficiency and effectiveness of test automation, in line with the changing expectations in this fast-paced environment.

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Unleashing the Power of AI in End-to-End Testing: A Paradigm Shift in Transformative Testing

Suhas BM, Scrum Master, FireFLink Pvt. Ltd.
Sudarshan Kumar T, Senior Test Engineer, FireFLink Pvt. Ltd.

In the current advancing domain of software development, the importance of streamlined testing procedures cannot be emphasized enough. End-to-end testing, specifically, holds a crucial position in guaranteeing the smooth operation of applications across a range of scenarios. The emergence of Artificial Intelligence (AI) has brought about a revolutionary change in the testing landscape, introducing an era marked by unprecedented speed, precision, and inventive approaches.

Problem statement

1. Firmware Over-the-Air (FOTA) Updates:

Establishing a robust automation framework for Firmware Over-the-Air (FOTA) updates in embedded systems, particularly IoT devices, involves automating package validation, secure transmission, installation verification, and post-update system stability. This encompasses addressing software aspects, network conditions, potential interruptions, and implementing effective rollback mechanisms for failure

2. Security Testing for IoT Devices:

With the expansion of IoT devices, security is a most important concern. Designing an end-to-end automation framework for security testing in embedded systems involves simulating various attack scenarios, encrypting communication channels, and validating secure boot processes. This challenge extends beyond traditional functional testing, requiring automated tools to identify vulnerabilities, assess encryption strength, and ensure that security mechanisms operate effectively without compromising the system's performance.

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Innovations in Testing using AI

Author: Ritesh Kumar, Senior Test Engineer, Indium Software

Revolutionizing STLC with AI

AI techniques have demonstrated their value in software testing by significantly improving various aspects of the Software Test Life Cycle (STLC). They are particularly beneficial in automating test processes, optimizing test case design, and enhancing the overall quality of software products.

Enhancing Requirement Understanding with AI

AI driven requirement analysis is a transformative approach that leverages the capabilities of artificial intelligence tools to enhance the efficiency and quality of processes like requirement analysis and documentation.

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Unleashing the Power of AI: Innovative Ways to Generate Test Data

Shilpa Saha, Software Quality Control Specialist, Shell India Pvt Ltd
Parth Venkatesh, Software Quality Assurance Specialist, Shell India Pvt Ltd
Suraj Malli, Associate Software QA specialist, Shell India Pvt Ltd

Testing is a critical phase that ensures the quality, reliability, and performance of software applications. As projects become more complex and timelines tougher, the need for efficient and effective test data generation has grown considerably. This is where Artificial Intelligence (AI) steps in, changing the way we create test data.

Manual Test Data Creation: Creating test data is challenging and error and a manual process. This method becomes increasingly impractical as projects grow in complexity, demanding a more scalable and efficient approach.

Data Privacy and Security Concerns: Sometimes fetching test data raises privacy and security concerns, especially in industries where sensitive information is involved. Static Test Data: This limitation poses a risk of overlooking issues that arise from dynamic changes in application behavior.

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AI-led End-to-End Testing

Author: Murali Krishnan, Consultant Delivery Manager, CloudScaleQA


We are still in the process of understanding AI , QE and the impact of AI on various aspects of our life. AI in testing can be explained in simple terms of simulating Human Intelligence in a structured way, with a lot of historical data points and past experience. This post attempts to dwell further into how AI may impact the SW testing in the technology world, the areas where AI is expected to aid and drive End to End testing.

Testing Intelligence:

As a Tester, human intelligence is already being used in many of the following processes and areas of the testing life cycle:

  • Test planning
  • Estimation of testing effort and deriving the testing schedule, based on past experience and historical data
  • Deriving a most suitable Test Approach
  • To identify Risks and mitigation plans
  • Identify Product risks, Project risks
  • Identify and implement Test design techniques to be used in a testing project, example arriving at an optimum number of test cases using Decision Table technique based on a relevant requirement
  • Shift Left into the Business domain and arrive at Test Scenarios and tests well before testing commences, similar to an ATDD
  • Collate Metrics and Metrics analysis for corrective actions
  • Arrive at processes for Defect prevention, to implement Quality Engineering
  • Identify Continuous improvement areas
  • In the areas of test automation – automation suitability analysis, Timing, Automation tool evaluation, Automation ROI,

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