Software Quality Assurance: The Backbone of Business Resilience

Julia Teryokhina circle

Julia Teryokhina, Senior Presales Engineering Manager

September 4, 2024

That’s a lot of zeros! 



Key principles of software testing and quality assurance

1. Quality begins with specification

2. Software quality control throughout the development lifecycle

Enhancing software quality management involves more than just reviewing specifications. It includes key practices such as:

  • Test-Driven Development (TDD), where tests are created before writing code to ensure reliability and reduce errors.
  • Behavior-Driven Development (BDD) extends TDD by emphasizing natural language scenarios and examples, fostering better understanding and communication between non-technical stakeholders and the engineering team.
  • Code reviews, where peers check each other’s code for quality and share knowledge.
  • Technical debt control, focusing on continuously improving code to prevent future issues.
  • Security testing to identify and fix vulnerabilities, safeguarding against cyber threats.
  • Performance testing, which includes load testing to assess response under heavy user traffic and stress testing to evaluate limits and endurance.
  • Usability testing, starting from the UI design phase and continuing through user acceptance testing.
  • Constant monitoring of software availability in production and optimizing where necessary to maintain or improve efficiency.
  • Reviewing user feedback and incorporating necessary improvements to enhance user experience and satisfaction.

Adopting these practices, collectively forming Quality Driven Development (QDD), requires significant effort for both execution and ongoing improvement. Integrating various AI assistants into these processes can help automate tasks, provide insights, and enhance overall quality control efforts effectively.

3. CI/CD for software quality assurance



4. Employee professional development

5. Independent QA teams



QDD benefits

Quality driven development offers significant advantages by prioritizing rigorous software testing and quality assurance practices. Here they are:

  • Enhanced quality via comprehensive testing methods and proactive software quality management throughout the development process.
  • Early detection and resolution of issues with techniques like TDD and BDD, which prevent costly fixes later on.
  • Continuous improvement achieved by implementing and automating best practices.
  • Closer collaboration between QA teams and developers ensures that software meets customer requirements.
  • Strengthened security and better performance achieved through automated security and load testing, protecting software from cyber threats and vulnerabilities.
  • User retention is boosted by delivering an attractive, high-standard product. Studies indicate that users are less loyal to software with bugs; more than half would abandon an app if they encountered significant issues in a day. Therefore, high-quality software not only builds a strong brand but also retains and expands the user base.

Thus, quality driven development results in reliable and competitive software products.

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QDD risks

Quality driven development poses several risks that organizations must consider:

  • Increased development time due to meticulous testing and code reviews. Additionally, maintaining high-quality documentation demands extra effort.
  • Higher initial costs from investing in specific tools for automated testing and QA analysis. Continuous team education on QDD practices also adds to expenses.
  • Potential overhead, as the focus on quality may not always justify the investment. This also correlates with the risk of over-engineering, leading to unnecessary features and costly, overly sophisticated solutions.
  • Resistance to change among team members and stakeholders, requiring persuasion of the long-term benefits.
  • Measurement challenges in quantifying quality, as assessments are often subjective.
  • Adherence to QA standards among external teams may be complicated if the company turns to outsourcing. To achieve effective coordination, robust communication is essential.

Despite all these risks, a proper development approach promises long-term benefits, such as enhanced software quality and reduced technical debt. Still, organizations can harness QDD effectively with the help of strategic planning and effective management.

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Employing AI tools to ensure quality


1. Better project planning

2. Creating robust specifications

3. Crafting exceptional UI/UX designs

4. Enhancing architecture quality

5. Improving code quality

6. Generating unit tests automatically

Beyond well-known GitHub Copilot, try using one of these AI tools tailored to specific programming languages: Developers can utilize Diffblue Cover to generate unit tests for Java and Kotlin programs or DeepUnit.ai for creating unit tests in TypeScript projects.

7. Streamlining testing processes

To optimize testing approach, leverage one of the following AI-powered platforms: Testim applies artificial intelligence to develop and maintain automated tests while spotting potential issues in advance of production deployment.

8. Enhancing the quality of support services

Finally, your application is released to production! 
By
employing artificial intelligence at every stage of development—from project planning to processing user feedback—organizations can ensure higher quality, improved efficiency, and better user satisfaction.

Software quality metrics

The following software metrics are worth considering. 

Metric

Description

Defects per requirement, or defect density

Measures how many defects are discovered per requirement during testing, indicating their risk level for release. It helps teams prioritize improvements to testing strategies and decide on the deployability of each requirement based on its defect count.

Bugs found vs. bugs fixed

Evaluates QA effectiveness by comparing discovered bugs to resolved ones, offering insights into defect management and testing progress.


Bugs found vs. Bugs fixed = ( Total number of defects reported / Total number of defects fixed) x 100%

Defect resolution percentage

Measures the efficiency of the development team in analyzing and fixing bugs reported by QA teams. By tracking the ratio of defects fixed to those reported, this metric can help explain delays in shipping and is particularly useful in discussions with management. 


Defect resolution % = (Total number of defects fixed / Total number of defects reported) x 100%

Fix quality (or defective fixes %)

Evaluates the effectiveness of fixes in maintaining software reliability, crucial for ensuring customer satisfaction in critical systems. It quantifies defective fixes as a percentage of all fixes within a given period, aiming for zero defective fixes within the analyzed period.


Fix quality % = (Total number of defective fixes / Total number of defects fixed ) x 100%

Cost per bug fix

Represents the amount spent to fix each bug, calculated as the time taken to fix the bug multiplied by the developer's hourly rate. For a more comprehensive figure, you can also include the cost of testing each bug fix in the final reporting. 


Cost per bug fix = (Time taken to fix + Time taken to validate fix) * the avg team member’s hourly rate.

Defect age

Measures the average time taken to fix a defect, calculated from the time of bug creation to its resolution, typically measured in days. A progressively low Defect Age indicates maturing QA processes, as bugs are resolved more quickly with each test cycle. 


Defect age = Difference between the time of bug creation and time of bug resolution.

Number of reopenings

Tracks how often a particular task or bug is reopened, even if it happens across different builds or releases. Frequent reopenings indicate system instability, architectural design issues, and low code quality.

Defect distribution over time

Charts the number and origin of defects at the end of each test cycle, showing the development team's progress in identifying and resolving bugs. Categorizing defects by cause, module, severity, or platform helps pinpoint areas needing more attention, making it easier to identify and address recurring issues in specific categories.

Fix backlog and Backlog Management Index (BMI)

The Fix Backlog metric tracks the rate of defect arrivals against the rate at which fixes for reported problems are made available. It measures the count of unresolved problems at the end of each month or week. The Backlog Management Index (BMI) helps manage this backlog, indicating backlog reduction when BMI is greater than 100 and an increase when BMI is less than 100.

BMI = (Number of defects closed within the period / Number of defects reported within the period) x 100%

Escaped bugs

Measures post-release software issues reported by customers, indicating QA effectiveness. Minimizing these bugs ensures smoother user experiences and fewer post-launch interruptions.

Mean time to failure

Measures the average time between system failures, commonly used in safety-critical systems such as air traffic control, avionics, and weaponry. This metric helps assess the reliability and performance of systems where failure can have severe consequences.

Customer problems

Measures the issues customers encounter while using the product, including both defect-oriented and non-defect problems, from the customer's perspective. This metric is usually expressed as Problems per User-Month (PUM), calculated by dividing the total problems reported by customers over a time period by the total number of license-months of the software during that period.


PUM = (Total problems that customers reported for a period) / (Number of installed licenses × Number of months in the analyzed period)

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