Defect Removal Efficiency: How To Calculate It For Test Automation

May 10, 2024Raunak Jain
Defect Removal Efficiency How To Calculate It For Test Automation

Start automating your tests 5X Faster in Simple English with Testsigma

Try for free

In today’s fast-paced software development landscape, a well-structured defect removal strategy holds paramount importance. At the heart of this strategy lies the concept of Defect Removal Efficiency (DRE). Defect removal efficiency quantifies the effectiveness of identifying and rectifying defects during the testing phase, playing a pivotal role in enhancing the overall software quality.

This proactive approach not only minimizes the chances of defects slipping through to production but also reduces the time and effort required for rectification, ultimately resulting in a more streamlined and cost-effective development process.

A strategic integration of DRE within test automation practices ensures that the automated tests themselves are effective in identifying defects. This symbiotic relationship empowers development teams to not only detect defects early but also to make informed decisions on the quality of the software, thus enabling them to prioritize and address issues efficiently. 

In the subsequent sections, we will delve deeper into the intricacies of calculating Defect Removal Efficiency for test automation, equipping you with the knowledge to bolster your defect removal strategy and enhance your software quality.

Defect Removal Efficiency

Defect Removal Efficiency (DRE) is the litmus test of your software testing prowess, encapsulating the effectiveness of your defect identification and resolution endeavors. Think of DRE as a magnifying glass that zooms in on the accuracy and thoroughness of your testing process. It’s not merely about spotting defects; it’s about the precision with which you eradicate them.

Imagine a scenario where a critical software application is about to be deployed for a major online shopping platform. A seemingly minor glitch in the payment gateway could lead to a cascade of abandoned transactions, disgruntled customers, and reputational damage. Here, DRE steps in as the vigilant guardian, ensuring that such potential pitfalls are detected and rectified before they can wreak havoc.

DRE’s significance extends beyond avoiding cataclysmic disasters. Let’s take a stroll through the realm of medical software. In the world of electronic health records, even a small coding anomaly can jeopardize patient care and safety. An efficient DRE in this context becomes a patient advocate, verifying that the software functions seamlessly, thereby preventing medical errors and ensuring accurate diagnoses.

This efficiency metric isn’t just about numbers; it’s about foresight. Just as a vigilant gardener prunes a plant to foster healthy growth, a high DRE prunes defects to nurture robust software. It aids in prioritizing fixes, allocating resources judiciously, and achieving the holy grail of software development: delivering a reliable product that delights users. 

The Defect Removal Efficiency Formula

The Defect Removal Efficiency (DRE) formula acts as a compass in determining and quantifying the quality of your software product. 

The formula for computing the defect removal efficiency is: 

DRE (%) = [Total Defects Found in Testing / (Total Defects Found in Testing + Total Defects Found in Production)] x 100

In this formula, the parameters are:

Total Defects Found in Testing: The number of defects discovered during the testing phase of software development.

Total Defects Found in Production: The number of defects reported by users or detected after the software has been released to the production environment.

Let’s illustrate the formula with a real-life example:

Imagine a mobile application development project. During rigorous testing, the QA team identifies 120 defects. However, after the app is launched, users report an additional 30 defects. To calculate the DRE:

DRE = (120 / (120 + 30)) x 100 = 80%

This means that 80% of defects were successfully removed during the testing phase, indicating a substantial level of efficiency in the defect removal strategy. The remaining 20% of defects found in the production environment could signify areas for improvement in the testing process or further refinement of the defect removal strategy.

Understanding and applying the DRE formula empowers development teams to quantitatively assess their defect management efforts. As this formula serves as a powerful diagnostic tool, it aids in pinpointing areas that require attention and fine-tuning. Aiming for a higher DRE percentage translates into a more robust and dependable software product, bolstering user satisfaction and organizational reputation.

Check out this article on defect leakage to determine the number of undetected bugs in software testing.

How to Calculate Defect Removal Efficiency?

Calculating Defect Removal Efficiency (DRE) requires a systematic approach that captures the essence of your defect identification and resolution efforts. Follow these steps to compute DRE and gain valuable insights into the efficacy of your defect removal strategy:

  1. Gather Data: Collect data on the total number of defects found during both the testing phase and in the production environment. Ensure accurate and comprehensive recording of defects.
  1. Apply the Formula: Utilize the DRE formula to calculate the percentage. The formula is as follows:

DRE (%) = [Total Defects Found in Testing / (Total Defects Found in Testing + Total Defects Found in Production) ]x 100

  1. Interpret the Result: The calculated DRE percentage indicates the proportion of defects effectively removed during testing. A higher DRE signifies a more efficient defect removal strategy, while a lower DRE prompts a closer examination of testing processes and strategy.
  1. Continuous Improvement: Regularly calculate DRE for various projects and development cycles. Analyze trends and patterns to identify areas for improvement and to fine-tune your defect removal strategy.
  1. Incorporate Insights: Leverage DRE insights to make informed decisions about resource allocation, testing methodologies, and overall software quality enhancement.

By following this step-by-step guide, you can consistently measure and monitor Defect Removal Efficiency, ensuring a proactive and data-driven approach to defect management within your software development lifecycle.

Interpreting Different Outcomes of the Defect Removal Efficiency (DRE) Formula

Analyzing the outcomes of the DRE formula provides valuable insights into the quality of your testing and defect management processes. Let’s explore the different possible outcomes and their implications:

High DRE (Close to 100%):

A high DRE indicates that a significant proportion of defects were identified and rectified during the testing phase. It implies that the majority of defects are being caught before they reach the production environment, reducing the risk of post-release issues. 

High DRE is a testament to a well-structured defect removal strategy and signifies a strong alignment between testing efforts and software quality goals.

Moderate DRE (Between 50% – 80%):

A moderate DRE suggests that a considerable number of defects were identified during testing, but there is room for improvement. This outcome may indicate that some defects are slipping through to the production environment, warranting a closer examination of testing processes and defect management strategies. 

Organizations with moderate DRE may benefit from refining test case coverage, optimizing automated testing scenarios, and strengthening defect-tracking mechanisms.

Low DRE (Below 50%):

A low DRE highlights that a substantial number of defects are going undetected during testing and are surfacing in the production environment. This outcome is a red flag for potential quality issues, user dissatisfaction, and increased post-release support efforts. 

A low DRE highlights the need for a comprehensive review of the entire defect removal strategy. It may necessitate revisiting test case design, improving test data accuracy, and fortifying automated testing practices.

In essence, these outcomes steer quality improvement initiatives, shape resource allocation decisions, and ultimately contribute to the delivery of robust and reliable software products. 

The continuous monitoring and analysis of DRE outcomes facilitate a proactive and data-driven approach to software quality assurance.

How to Calculate Defect Removal Efficiency for Test Automation?

Harnessing the power of automation to calculate Defect Removal Efficiency (DRE) brings a new dimension to software quality assurance. The synergy between automated testing and DRE provides a precise measure of defect identification and eradication within the automated testing framework.

  1. Comprehensive Test Suite Design:

Develop a diverse and extensive set of automated test cases that cover various functionalities and scenarios. Craft tests that mimic user interactions to ensure realistic defect detection. Prioritize high-impact and frequently used features to maximize defect identification.

  1. Test Automation Tool Integration:

Utilize specialized automated testing tools to facilitate efficient test script creation, execution, and maintenance. Leverage features like data-driven testing and parameterization to enhance test coverage and defect identification.

If you are looking for an end-to-end AI-driven automated testing tool, you should definitely check out Testsigma. It has high-end features such as creating simple test cases in the natural English language, testing in 2000+ real-device combinations on the cloud, parallel testing, integration with CI/CD tools, etc. In fact, features such as customizable reports, and collaborative setup can highly aid in computing DRE and sharing results among the team.

Automate your end-to-end UI tests, 5x faster, and for improved efficiency, with Testsigma

Check out Testsigma
  1. Continuous Integration and Continuous Testing (CI/CT):

Integrate automated tests into your CI/CT pipeline to ensure consistent execution with each code change. Automate the execution of tests on multiple configurations and platforms for comprehensive defect detection.

  1. Regression Testing Automation:

Implement automated regression tests to swiftly identify regressions and defects that resurface after code changes. Continuously monitor the software for unexpected behavior and defects in the automated regression suite.

  1. Defect Tracking and Reporting:

Integrate defect tracking tools to seamlessly capture and manage defects detected by automated tests. Generate detailed defect reports with information on severity, steps to reproduce, and automated test references.

  1. Continuous Monitoring and Analysis:

Regularly monitor automated test execution results to track defect detection trends and patterns. Analyze the data to identify common defect areas, assess defect removal effectiveness, and make data-driven decisions.

  1. Continuous Improvement Loop:

Utilize DRE calculations as a performance benchmark to drive continuous improvement efforts. Review DRE trends over time to measure the impact of process changes and optimizations on defect removal.

  1. Collaborative Approach:

Foster collaboration between development, testing, and quality assurance teams. Use DRE as a common metric to align goals, identify challenges, and prioritize defect removal initiatives.

By intertwining automated testing strategies with the calculation of Defect Removal Efficiency, you create a dynamic feedback loop that not only quantifies your defect management efforts but also propels your software development towards higher quality, improved efficiency, and a seamless user experience.

Advantages of Defect Removal Efficiency (DRE):

  • Early Defect Detection: A high DRE ensures that defects are caught early in the development cycle, minimizing the risk of costly and time-consuming rectifications down the road.
  • Enhanced Software Quality: Effective defect removal directly translates to improved software quality, resulting in a more reliable and user-friendly product.
  • Cost Savings: Identifying and rectifying defects early reduces the overall cost of development by preventing issues from proliferating and becoming more complex.
  • Time Efficiency: High DRE accelerates the development process by reducing the need for extensive rework, allowing for quicker release cycles and faster time-to-market.
  • User Satisfaction: A low-defect software product leads to higher user satisfaction, better user experience, and positive brand perception.

Disadvantages of Defect Removal Efficiency (DRE):

  • Overemphasis on Numbers: Relying solely on DRE metrics can lead to a narrow focus on numerical targets, potentially overlooking the qualitative aspects of software quality.
  • False Sense of Security: A high DRE might lead to complacency if not accompanied by comprehensive testing, creating a false sense of security regarding software robustness.
  • Resource Constraints: Striving for an extremely high DRE could strain resources, diverting efforts from other critical aspects of development such as feature enhancement.
  • The complexity of Measurement: Measuring DRE accurately can be intricate, as it involves tracking defects across various stages of development and accounting for different severity levels.
  • Diminished Innovation: An excessive fixation on defect removal might stifle innovative experimentation and risk-taking in the development process.

Balancing the advantages and disadvantages of Defect Removal Efficiency is essential to crafting a well-rounded defect removal strategy that aligns with your organization’s goals and development approach.


In the ever-evolving landscape of software development, Defect Removal Efficiency (DRE) stands as a beacon of quality assurance, guiding organizations to navigate the complexities of defect identification and resolution. This crucial metric encapsulates the essence of effective defect management, ensuring that software products emerge robust, reliable, and user-centric.

Automated testing emerges as a dynamic ally in the pursuit of enhanced DRE. With a strategic integration of DRE calculations within the realm of automated testing, organizations unlock a treasure trove of insights that magnify the impact of defect removal efforts.

Automated testing, coupled with streamlined defect tracking and reporting, forms the backbone of a proactive approach, allowing teams to address issues before they ripple into production.

As the curtain falls on this discourse, the symbiotic relationship between Defect Removal Efficiency and automated testing remains illuminated. Each automated test executed, each defect detected, and each calculated DRE percentage propels the software development process toward a future where defects are minimized, quality soars, and user satisfaction reigns supreme.

Frequently Asked Questions

What is Defect Removal Rate?

Defect Removal Rate (DRR) is a metric that quantifies the speed and efficiency at which defects are identified and resolved during the software development process. It measures the rate at which defects are removed relative to the total number of defects discovered. A higher DRR indicates a more agile and effective defect-removal process, contributing to improved software quality and user satisfaction.

What is Zero Defect Rate?

Zero Defect Rate (ZDR) is an aspirational concept in software development that aims to achieve a state where no defects are present in the final product. It signifies a level of quality assurance excellence where software is released with no known defects. While achieving a true zero defect rate might be challenging, organizations often strive to get as close as possible by implementing stringent testing, thorough code reviews, and continuous quality improvement practices. ZDR emphasizes the commitment to delivering software that is highly reliable, robust, and aligned with user expectations.

Subscribe to get all our latest blogs,
updates delivered directly to your inbox.


What is Automation Scalability? Things You Should Know


What is an Automated Test Suite & How to Create it?


Hiring Talented Software Testers: Unraveling the Secrets to Effective Hiring