Developing Quality Software Competency

Which roles would benefit from mastering this competency?

Software Engineers, Quality Engineers, System and Solution Architects, Product Owners, Scrum Masters, Release Train Engineers, team-level technical coaches, and SPCs.

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Learning about the Developing Quality Software Competency

The following resources are suitable for beginning your learning journey in the Developing Quality Software competency:

Applying the Developing Quality Software Competency

Here are some tips for beginning to apply quality software practices:

• Learn and adapt – Be open to learning new practices and tools. Adapt your approach based on what works best for your team.
• Start with the basics – Focus on fundamental practices like Test-Driven Development (TDD). These help catch issues early and involve all team members in practicing together.
• Set Clear goals – Define what quality means for your Agile Team. Establish clear acceptance criteria for features and stories that include new techniques that are proving to work for you.
• Automate testing – Automate tests to ensure code quality. Don’t be shy about augmenting your work with AI tools that speed up your work and enable you to have more time for innovation and system stability.
• Encourage collaboration – Foster a culture of teamwork. Encourage your team members to share knowledge and pair work.
• Gather feedback – Regularly seek feedback from your teammates, users, and stakeholders. This helps identify areas for improvement.
• Measure progress – Track quality metrics to see how well your practices are working. Use this data to make informed adjustments.

Mastering the Developing Quality Software Competency

When quality software development practices are applied across Agile Teams with mastery, the following should begin to be prevalent:

• Defined processes – Teams have clear definitions of done and acceptance criteria for their work, including applying chosen practices. This ensures everyone understands what is expected.
• Quality focus – Teams consistently deliver high-quality software. They use practices like Test-Driven Development and Spikes to catch issues early.
• Frequent feedback – Teams regularly seek feedback from customers. This helps them understand what works and what needs improvement.
• Continuous improvement – Teams track their performance metrics. They use this data to fuel retrospectives and set new goals for improvement. Refactoring and eliminating technical debt are standard parts of allocated capacity within teams.
• Collaboration – Team members work closely together, sharing knowledge and skills. This creates a stronger product and a resilient workforce to emerging technologies.
• Adaptability – Teams quickly respond to changing requirements and feedback. Their technical practices assist these shifts. As technology evolves and organizational policies permit, more engineers use AI to adapt to new trends and tools, keeping their skills relevant.

Nimbus Networks’ Quality Culture

Nimbus Networks faced a new challenge: ensuring true quality in their rapidly evolving software, data, cloud, and AI infrastructure. The “Creating Great Agile Teams” initiative had empowered their teams, but frequent post-release defects and escalating maintenance costs highlighted a need for more.

Enter the “Developing Quality Software Competency.” Maria, a seasoned architect, championed this next phase. She saw that while teams were faster, the quality wasn’t consistently built in. “It’s like building a beautiful house quickly, but forgetting to check the foundation,” she explained to her team.

The first step was to embed Test-Driven Development (TDD) into every software team’s workflow. This meant writing tests before the code, a radical shift for some. The initial resistance was met with clear goals and dedicated coaching from Quality Engineers. Soon, developers started seeing fewer bugs slip through, and their confidence grew.

Next, the data teams focused on Behavior-Driven Development (BDD). They began writing clear, business-focused scenarios for their data pipelines and AI models. This helped bridge the gap between what the business needed and what the data engineers built. For example, before an AI model was deployed to recommend cloud services, the BDD approach ensured everyone agreed on “Given a customer with X history, when they browse Y services, then the AI should recommend Z with 90% accuracy.” This clarity drastically improved the reliability of their AI system’s output.

For the cloud infrastructure teams, applying the competency meant rigorous attention to Nonfunctional Requirements (NFRs). Performance, security, and scalability became non-negotiable aspects defined and tested early in the development cycle. They started using automated testing tools, augmented by AI, to constantly monitor the health and stability of their cloud environments. This proactive approach helped them identify and resolve vulnerabilities faster than ever before.

Collaboration was paramount across all teams. Software engineers, data scientists, and cloud specialists began pair programming and cross-training. Spikes, defined as exploration and research activities, were used to tackle complex technical challenges, reducing risk before major development efforts. Refactoring became a continuous practice, regularly cleaning up code and infrastructure to prevent technical debt from accumulating.

Regular feedback loops were established, not just from users but also internally. Team retrospectives evolved to analyze quality metrics deeply, leading to continuous improvement. If a new defect cropped up, the teams not only fixed it; they investigated the root cause and adjusted their practices to prevent recurrence.

Within months, Nimbus Networks saw a dramatic reduction in post-release defects and maintenance costs. Critical software vulnerabilities were resolved with unprecedented speed. Most importantly, trust in their AI systems soared. Customers experienced fewer glitches, and the AI models consistently delivered accurate and reliable results. The journey continued, proving that resetting their agility practices was just a first step; building quality into everything they did was a valuable additional focus.

Continuing Your Journey through the Team and Technical Agility Discipline

Last Update: 13 February 2026