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Beyond Theory: Practical Steps for Writing a Robust Automation PoC

Nov 16th 2023 24 min read

In the realm of software development, a Proof of Concept (PoC) serves as a pivotal tool to validate the feasibility and potential success of a proposed solution. It is a tangible demonstration that allows stakeholders to assess the viability of an idea before committing to full-scale development.

A PoC typically involves implementing a small-scale version of the envisioned solution to showcase its functionality, performance, and benefits. While the fundamental principles of writing a PoC apply universally, regardless of the specific software application, nuances arise when tailoring a PoC for automation. In this blog post, we'll delve into the intricacies of crafting a PoC for automation, exploring practical steps and considerations that distinguish it within the broader landscape of software development.

Throughout this blog post, our guiding example for illustrating the intricacies of crafting a PoC for automation will be the implementation of visual regression testing - a sophisticated method aimed at fortifying the QA processes in web application development.

Executive Summary

In the context of a PoC, the Executive Summary serves as a concise introduction outlining the purpose and objectives of the demonstration. It encapsulates the essence of the PoC, providing a high-level overview for stakeholders to grasp its significance.

The primary goal of our example PoC is to showcase the feasibility and advantages of implementing visual regression testing to augment the QA processes in web application development. This entails addressing challenges associated with manual inspections and time-consuming regression testing. The aim is to streamline QA efforts, reduce manual workload, and improve the accuracy of detecting visual defects within the web application.


Before delving into the example, let's elucidate what visual regression testing automation entails. Visual regression testing involves the automated comparison of screenshots between different versions of a web application to identify unintended visual discrepancies. In essence, it acts as a vigilant guardian, meticulously examining the user interface to ensure that changes in the application code do not introduce unexpected visual defects. As we embark on this PoC journey, our focus will be on demonstrating how this innovative approach to QA can be seamlessly integrated into the web development lifecycle.

Background Information

This section is designed to provide a comprehensive background on the existing QA process, shedding light on its inherent challenges. The primary focus is on articulating the difficulties posed by manual visual inspections, the time-consuming nature of regression testing, and the evolving demands of contemporary web application development.


Within the current QA landscape, challenges stem from the manual nature of visual inspections and the resource-intensive process of regression testing. Visual inspections, reliant on human observation, risk overlooking subtle defects as applications become more intricate. Simultaneously, the laborious task of regression testing grows in complexity with codebase evolution, causing delays in identifying potential issues. This section aims to set the context for the PoC by highlighting these challenges and the compelling need for a more efficient and accurate QA solution.

Problem Statement

In this crucial section, we delve into a precise articulation of the challenges embedded in the current QA process. The goal is to spotlight specific pain points, encompassing the risk of unnoticed visual defects, the labor-intensive nature of regression testing, and the demand for a modernized, automated, and visually intuitive verification approach.


The challenges in the current QA process are multifaceted, starting with the inherent potential for visual defects to slip through the cracks. Relying on manual inspections, particularly as web applications grow in complexity, introduces a heightened risk of subtle visual discrepancies going unnoticed.

Simultaneously, the manual effort involved in regression testing stands as a significant impediment. As the codebase evolves, executing comprehensive regression tests becomes increasingly time-consuming, impeding the agility of the development lifecycle and delaying the identification of potential issues.

Despite the challenges faced by the QA landscape, there is a clear desire for a more automated and visually intuitive verification approach. Stakeholders yearn for a solution that can not only mitigate the risk of undetected visual defects but also streamline the regression testing process, ushering in a new era of efficiency and accuracy.

As we proceed through this PoC, we aim to address these challenges head-on, demonstrating how visual regression testing automation can be the transformative solution sought in the realm of QA.

Goals and Objectives

In this pivotal section, we articulate the specific goals and objectives that serve as the driving force behind our PoC. The aim is to provide a clear roadmap, outlining tangible achievements such as the reduction of manual effort in regression testing, early identification of visual defects in the development lifecycle, and an enhancement of overall test coverage.


Our PoC is strategically aligned with distinct goals designed to usher in efficiency and precision in the QA process. First and foremost, we target the reduction of manual effort in regression testing - a goal rooted in the acknowledgment of the time-consuming nature of traditional regression testing practices.

Simultaneously, the objective is to identify visual defects at an early stage in the development lifecycle. By integrating visual regression testing automation, we aspire to detect discrepancies promptly, preventing their escalation and minimizing potential impacts on the final product.

Moreover, a core goal involves improving the overall test coverage. This entails extending the scope of testing to critical user interfaces, ensuring a more comprehensive evaluation of the web application's functionality and visual integrity.

Through these defined goals and objectives, our PoC endeavors to showcase the tangible benefits of visual regression testing automation in addressing specific pain points within the QA process. As we progress, each goal will serve as a benchmark for success, contributing to the overarching aim of transforming the QA landscape.


In this section, we precisely delineate the scope of our PoC, outlining the specific boundaries within which our efforts will be concentrated. The focus is on implementing visual regression testing for a carefully selected subset of critical user interfaces. This deliberate scope aims to showcase the feasibility and effectiveness of automated visual checks in a controlled environment.


The scope of our PoC is meticulously defined to concentrate on a subset of critical user interfaces, strategically chosen to represent key aspects of the web application. This intentional narrowing of focus allows us to delve into the intricacies of visual regression testing within a controlled environment, providing a targeted and insightful demonstration.

Our emphasis on critical user interfaces serves as a practical illustration of the PoC's intended impact. By honing in on specific elements that are pivotal to the user experience, we aim to demonstrate the feasibility and effectiveness of automated visual checks in detecting potential defects and ensuring visual integrity.

As we embark on this focused exploration, the defined scope acts as a guiding framework, allowing us to derive meaningful insights into the capabilities and advantages of visual regression testing automation. The outcomes within this delimited scope will contribute to a more comprehensive understanding of the potential application of this innovative approach in broader QA scenarios.

Solution Overview

Here, we offer a comprehensive overview of the proposed solution for our PoC. The focus is on the seamless integration of a visual regression testing tool into the existing automated testing framework. This tool acts as a vigilant guardian, capturing and comparing screenshots of web pages to discern visual differences - a process crucial for the early detection of potential defects.

The Solution Overview is an ideal juncture to exemplify the integration process with a code snippet. Demonstrating the practical implementation of the visual regression testing tool showcases its functionality within the existing automated testing framework.

Code Example

# Import necessary libraries for the automated testing framework
from selenium import webdriver
from visual_regression_tool import VisualRegressionTool
# Initialize the web driver for automated testing (e.g., Selenium)
driver = webdriver.Chrome()
# Initialize the Visual Regression Testing Tool
visual_tool = VisualRegressionTool()
# Function to perform a visual regression test
def perform_visual_regression_test(url, test_case_name):
    # Navigate to the specified URL
    # Capture a screenshot of the current state
    baseline_screenshot = visual_tool.capture_screenshot(test_case_name + '_baseline')
    # Perform actions on the web page (e.g., user interactions)
    # Capture a new screenshot after the actions
    new_screenshot = visual_tool.capture_screenshot(test_case_name + '_new')
    # Compare the new screenshot with the baseline
    visual_differences = visual_tool.compare_screenshots(baseline_screenshot, new_screenshot)
    # Output the visual differences or take further actions based on the results
    if visual_differences:
        print(f"Visual differences detected in test case '{test_case_name}'")
        # Additional actions (e.g., logging, reporting)
        print(f"No visual differences in test case '{test_case_name}'")
# Example of using the visual regression testing in a test scenario
perform_visual_regression_test("https://example.com", "homepage")
# Close the web driver

The heart of our PoC lies in the proposed solution, a strategic integration of a visual regression testing tool into the fabric of our existing automated testing framework. This symbiotic relationship is designed to elevate the QA process by introducing an automated mechanism for capturing and comparing screenshots of web pages.

Consider the following analogy to illuminate the essence of our solution: Just as a meticulous inspector scrutinizes two versions of a visual masterpiece to identify subtle differences, our visual regression testing tool meticulously examines web pages. The tool captures screenshots at different stages, forming a baseline for comparison. Subsequent captures enable the detection of any visual differences, acting as a sentinel against unintended alterations.

This proposed solution is poised to revolutionize the QA landscape, providing an efficient and accurate means of verifying the visual integrity of web applications. As we progress through the PoC, this solution serves as the linchpin, demonstrating the transformative potential of visual regression testing automation in enhancing the quality assurance process.

Success Criteria

In this critical section, we establish the yardsticks by which the success of our PoC will be measured. The success criteria serve as tangible benchmarks, outlining specific metrics such as the reduction in manual effort for regression testing, the precision in identifying visual defects, and an overall improvement in confidence regarding the visual quality of releases.


The success of our PoC hinges on measurable criteria that align with the overarching goals. First and foremost, we aim for a discernible reduction in manual effort invested in regression testing. By quantifying the time saved and the efficiency gained through automation, we can tangibly demonstrate the transformative impact of our approach.

Another pivotal metric involves precision in identifying visual defects. The success of our PoC will be reflected in the tool's ability to detect subtle visual discrepancies early in the development lifecycle, mitigating the risk of defects escalating into critical issues.

Furthermore, the success criteria encompass an improvement in confidence regarding the visual quality of releases. This entails a qualitative assessment of how well the visual regression testing tool aligns with the visual expectations, bolstering trust in the reliability of the QA process.

These success criteria are not just benchmarks; they are milestones that, once achieved, will signify the tangible benefits of visual regression testing automation. As we progress through the PoC, each criterion becomes a litmus test, guiding us toward a comprehensive understanding of the effectiveness and viability of our proposed solution.

Implementation Plan

In this section, we lay out a detailed roadmap - the Implementation Plan - for the successful execution of our PoC. The plan meticulously outlines the steps involved in implementing visual regression testing. This includes the critical tasks of selecting a suitable tool, seamlessly integrating it with the existing test framework, and establishing visual baselines for effective comparison.


The path to executing our PoC is intricately detailed in the Implementation Plan, a strategic guide that ensures a systematic and effective implementation of visual regression testing. Here is a step-by-step illustration of the key tasks:

By following this Implementation Plan, we aim to navigate the complexities of implementing visual regression testing with precision and efficacy. Each step is a crucial building block, contributing to the successful execution of our PoC and providing valuable insights into the potential integration of visual regression testing into broader QA practices.

Technical Requirements

This section outlines the specific Technical Requirements essential for the successful execution of our PoC. It delineates the necessary components and conditions, including the need for a visual regression testing tool, compatibility with the web application stack, and integration with the continuous integration/continuous deployment (CI/CD) pipeline.


By explicitly defining these technical requirements, we establish the foundational elements necessary for the successful implementation of visual regression testing in our PoC. Each requirement serves as a critical building block, contributing to the robustness and effectiveness of our automated visual verification approach.

Risk Analysis

This section delves into a meticulous Risk Analysis, identifying potential challenges and uncertainties that may arise during the execution of our PoC. Risks such as false positives in visual comparisons, tool compatibility issues, and challenges in maintaining visual baselines are scrutinized. Additionally, mitigation strategies are outlined to proactively address and mitigate these risks.


By conducting a comprehensive Risk Analysis and providing proactive mitigation strategies, we aim to anticipate and address potential challenges. This approach ensures the resilience and success of our PoC, even in the face of uncertainties inherent in implementing innovative solutions like visual regression testing automation.

Evaluation and Testing

This section articulates the Evaluation and Testing methodology employed in our PoC. It provides a comprehensive understanding of how we assess the accuracy, reliability, and efficiency of the visual regression testing tool. Additionally, it outlines how visual regression testing seamlessly integrates with existing functional tests to provide a holistic assessment of our web application.


By detailing the Evaluation and Testing methodology, we aim to offer transparency into the robust assessment process of our visual regression testing implementation. This meticulous evaluation ensures that the tool not only meets its standalone objectives but also complements and enhances the effectiveness of existing functional tests.

Budget and Resources

This section delineates the Budget and Resources required for the successful implementation of our PoC. It provides a transparent estimate of the financial investment needed for acquiring the visual regression testing tool and outlines the necessary resources, including training for the QA team.


By providing a transparent budget estimate and resource allocation plan, we ensure a clear understanding of the financial investment required for the successful execution of our visual regression testing PoC. This enables effective planning and allocation of resources to support a seamless implementation process.

Project Team and Roles

This section delineates the key roles and responsibilities of the Project Team involved in the execution of our PoC. It specifies the individuals who will play crucial roles, including QA automation engineers, tool experts, and project managers, ensuring clarity on the division of responsibilities.


By specifying the roles of each team member, we establish a clear framework for collaboration and ensure that each individual's expertise is leveraged effectively to achieve the goals of our visual regression testing PoC.

Assumptions and Dependencies

This section outlines the Assumptions and Dependencies considered during the planning of our PoC. It provides transparency regarding the underlying assumptions guiding the PoC and dependencies that may impact its success.


By explicitly stating the assumptions and dependencies, we provide stakeholders with a clear understanding of the contextual factors that may influence the PoC's trajectory. This transparency fosters effective communication and proactive management of potential challenges.


This section outlines the expected Deliverables that will result from the successful execution of our PoC. It provides a roadmap of the tangible outcomes, including a comprehensive report on the PoC results, documentation on the visual regression testing process, and insights gained during the trial.


By outlining these deliverables, we establish a clear roadmap for communicating the outcomes and insights derived from the PoC. These tangible results contribute to a holistic understanding of the impact and feasibility of implementing visual regression testing in the QA process.


This section provides a detailed Timeline for each phase of our PoC. It outlines the schedule for planning, implementation, testing, and evaluation, offering stakeholders a clear roadmap of the PoC's progression.


By providing a detailed timeline, we ensure that all stakeholders have a clear understanding of the planned milestones and activities throughout the PoC journey. This facilitates effective project management and coordination among team members.

Conclusion and Next Steps

This section serves as a conclusion to our PoC, summarizing the findings and lessons learned. It emphasizes successful aspects and provides recommendations for the next steps, guiding stakeholders on potential enhancements and the integration of visual regression testing into the QA process.


By offering a comprehensive conclusion and outlining the next steps, this section ensures that stakeholders are well-informed about the outcomes of the PoC and provides a strategic direction for the integration of visual regression testing into the QA process.


The Appendices section serves as a repository for additional supporting materials that complement the PoC. It includes visual regression testing reports, comparison screenshots, technical specifications, and any other relevant documentation that provides in-depth insights into the PoC process.


Including these appendices enhances the PoC documentation by providing stakeholders with a wealth of supplementary materials. These materials offer a more granular view of the PoC process, supporting transparency and thorough understanding.

A well-executed Proof of Concept for QA automation testing provides a structured approach to assess the feasibility, benefits, and challenges of introducing automation into your testing process, ultimately contributing to improved software quality and efficiency.