Quality Assurance vs Quality Control in Modern Software

Quality Assurance And Quality Control In A Modern Software Environment

The terms “quality assurance” and “quality control” are some of the least understood, most searched-for subjects in today’s software-development community. Quite often, quality assurance and quality control are used interchangeably; however, they represent two completely different approaches to achieving a similar goal improving the quality of software products through the application of various processes, tools, and methodologies.

Quality assurance is important for software-engineering companies because it helps improve the processes used to build software so that they result in fewer costly defects. For SaaS companies in the U.S., as well as for rapidly scaling startup companies, the difference between quality assurance and quality control can have a significant impact on release cycles, the reliability of their products, the trust they build with customers, and the long-term engineering costs associated with maintaining their products.

By reviewing and clarifying the meaning of each term, this article will provide development teams with the information and insights they need to synergistically use both to develop more reliable and scalable products.

Quality Assurance In Software Development A Focus On Process

Quality assurance is a process-oriented discipline concerned with how software engineers develop software rather than simply what they develop. Quality assurance aims to identify potential defects early on, through the development and implementation of processes that result in a high-quality product. Quality assurance is designed to prevent defects by establishing standards, workflows, and processes that guide the development team in developing a high-quality end product from the ground up. Quality assurance for Software in today’s environment encompasses the following aspects;

• Testing strategies and quality standards.
• Testing and Development Processes.
• Testing Tool and Framework Selection.
• Agile and DevOps Pipeline Integration.
• Quality Metric Measurement Throughout The Lifecycle.

Quality Assurance functions during the early stages of product development. From Architecture Decision Making & Sprint Planning, to Acceptance Criteria and Product Release Readiness.

Essentially, Quality Assurance will ask;
Are we developing Software in the right way?

What Quality Control Means in Software Development?

Quality Control is Product-based. Quality Control focuses on the product, that is, what was produced by Software Development, and whether it meets the specifications & standards defined.

Quality Control is an activity that takes place after code is written to check for defects. This will determine that the features function correctly and produce the desired results, based on the defined conditions.

Quality Control in Software typically consists of:

• Feature Manual Testing.
• Automated Test Execution.
• Regression Test Execution.
• Defect Verification and Validation.
• Release Acceptance Testing.

Quality Control Usually Occurs After Quality Assurance (QA) & Typically During The Life Cycle Of Development Or After The Development Life Cycle.

Essentially, Quality Control will ask;
Does the Software Perform As Expected?

Quality Control versus Quality Assurance – The Differences

While both Quality Control and Quality Assurance share several similarities, there are three major differences between Quality Control and Quality Assurance, which include: differences in Purpose; timing (Quality Control occurrences happen after Quality Assurance); and differences in Scope. Concurrent Quality
As the name suggests, quality assurance covers concurrent processes of developing software, whereas quality control encompasses the actual software product.

Detecting Defects
Quality assurance’s main function is to eliminate or reduce defects caused by the software development process, while quality control detects existing defects via inspection and testing.

Future vs Present
Quality assurance focuses on preventing defects from occurring due to substandard processes. Conversely, quality control’s role is to validate existing defects.

Delegation of Responsibility
Quality assurance is a shared responsibility among engineers, product owners, and senior management. In contrast, testing teams are the principal owners of quality control.

Companies must comprehend the distinct differences between the two quality-related functions (i.e., QA & QC) to prevent making the common error of relying too heavily on one vs the other. Relying solely on QC is a “Going Out of Business” strategy.

How QA Benefits Agile and DevOps

As we move to more modern methods of software development (i.e., Agile and DevOps), QA has been incorporated into the daily work processes of software engineers. This means QA is part of the software engineer’s daily tasks rather than just an independent phase of the development process. The modern QA process may include:

• Shift-Left Testing
• Test-Driven Development
• CI Quality Gates
• Automated Testing Frameworks
• Risk-Based Testing Models

QA teams work collaboratively with engineers, product managers, and DevOps engineers throughout the software development life cycle to ensure that quality is consistently tracked and present at every stage of the software development process. Quality Assurance (QA) is a term that describes both how a company will achieve Quality and how that Quality will be measured. The traditional view of Quality Control was mainly to check or verify that Quality existed in a product; however, Quality Control is still an important process to ensure Quality in products.

Modern-day Quality Control looks at:

• Regression Testing on every build through Automation
• API & Integration Testing
• UI & Functional Testing
• Performance & Security Validation

Quality Control processes occur continuously and as part of the Continuous Integration and Continuous Deployment (CI/CD) Pipeline. Quality Control works alongside Development, rather than after the development process has been completed, allowing teams to find defects sooner in the process and continue to test at a high level.

An example of a Real World Comparison of QA to QC on a SaaS Application is listed below:

You have a SaaS application that will release an update weekly.

Quality Assurance for the application is:

• Standard of development coding
• Testing and other methods to ensure all automated coverage
• Acceptance Criteria at release
• Performance Benchmarks

Quality Control for the application is:

• Each release is tested against what was added in that release
• All previous functionality has been tested against regression
• Any defects found during testing have to be identified, triaged, and reported

The stronger the QA is, the lower the likelihood that any critical defects will be reported in the QC. Conversely, if QA is weak, QC will become buried, slowing down releases and increasing the risk.

This balance will be critical when scaling the business.

Here are some myths about QA & QC:

• QA = QC

The roles of QA and QC in an overall quality strategy are not the same. When companies confuse QA and QC, it can create voids in their overall quality strategy.

• Automation will replace QA.

Automation can assist QA; however, QA is about building systems and making decisions, not just giving people a tool.

• Start-Ups Don’t Need QA.

When companies are starting up, most benefit from an early QA process by recognising the potential costs of having to re-work a weak QA process.Quality Control vs. Quality Assurance in AI Software Quality Assurance
The complexity of AI-first products has added to the difficulty of understanding QA versus QC.

Quality Control vs. Quality Assurance in AI Software Quality Assurance

The complexity of AI-first products has added to the difficulty of understanding QA versus QC.

One way AI systems differ from earlier software systems is that they can change how they behave without changing their code. Because of this difference, quality control alone is insufficient in today’s environment.

To ensure AI Systems are functioning as expected, QA uses:

• Data Quality Verification
• Model Evaluation Standards
• An Assessment of Bias and Equity
• A Monitoring System for Model Drift

QC for AI Systems uses:

• Verification of Output
• Testing of Prompts
• Measuring the Performance of AI, relative to other AI systems

Thus, when it comes to building a trust relationship between QA and QC, QA builds the framework of trust, and QC establishes that trust.

QA and QC Metrics

Measuring quality in such a way that it is easy to measure Quality Assurance versus Quality Control requires two (2) Different Measurement Processes

Quality Assurance metrics include:

• Test Coverage Data
• The Rate of Issues Being Passed Back to the Development Team (Defect Leakage)
• The Rate of Compliance with Process
• An Assessment of Release Readiness

Quality Control Metrics include:

• Defect Density
• The Rate of Items that Did Not Pass
• The Amount of Time Required for an Issue to be Identified and Resolved
• The Impact of a Regression on an Existing Product or Service

Combined together, these Metrics allow One (1) set of Metrics to provide a complete view of a software product’s quality.

When a Quality Assurance Process Fails, the Quality Control Process is Required to Fill the Gap.

Many Organizations are Improperly Spending their Budget (Too Little on Quality Assurance and Too Much on Quality Control), Which Results in the Following:

• A Longer Period for Testing (Testing Takes Longer)
• More Late-Stage Defects Discovered
• Cumulative Fatigue in the Quality Control Testers (Testing Fatigue)
• A Longer Time Between Release of each new Version of the Product.

The quality control Function Can Not Substitute or Make Up for an Ineffective and/or Incomplete Quality Assurance Process. The quality of an AI system cannot be ensured by increased testing frequency or intensity, but by performing the required quality assurance activities accurately and thoroughly.

The Measurement of Future Success Will Continue to Be Built on the QA Process.
The Most Successful Teams Have a Balanced Approach to Quality Assurance and Quality Control. The following list represents a best practices list of quality ownership defined by individuals and teams within each other’s roles.

The above practices have resulted in an overall balance between speed and stability.

Quality assurance and quality control (QA and QC) are often debated between enterprise and startup teams within an organization.

Enterprise teams struggle with long QC cycles and rigid QA processes.

Start-up teams may avoid QA altogether and depend on reactive QC.

No matter what stage of development a business is in, both enterprise and startup teams can benefit from aligning QA and QC practices to meet the business goals.

Quality is not about being perfect; it is about being predictable.

At Logiciel Solutions, we believe that QA vs QC should not be a topic of debate; it should be viewed as a total system of processes.

QA is integrated into the architecture, planning, and delivery method of all Logiciel solutions.

Automation, measurement, and continuous improvement drive QC processes. This allows teams to provide faster delivery times while maintaining a lower level of risk.

The AI-first engineering model at Logiciel integrates quality into every step of the software development lifecycle (SDLC) for predictable releases and production-grade reliability.

Final Thoughts

The issue surrounding quality assurance vs quality control is not whether to choose one or the other, but rather understanding both roles and the interdependence of each.

Modern business requires that organizations create proactive quality assurance systems, which allow for the prompt and efficient verification of a product’s quality. Companies that master the QA/QC balance achieve faster time to market, fewer mistakes, and build trust over time.

At Logiciel Solutions, quality is part of each layer of the delivery of a product. If your organization is struggling with speed, stability, and/or scalability, your first step should be to evaluate your QA and QC strategy.

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