This plan aims to enhance software engineering research by focusing on specific metrics that are crucial for evaluating software quality and performance. The metrics include defect density, which highlights the importance of minimizing bugs for better code reliability. For example, implementing peer code reviews can significantly reduce defect density, ensuring the software's integrity.
Code coverage is another critical metric, emphasizing the need for thorough testing. Achieving a high percentage of tested code can prevent unexpected issues, with automated testing tools playing a vital role. Additionally, cycle time is measured to ensure that feature development is efficient and timely, impacting overall productivity.
Lastly, technical debt and customer satisfaction are included to maintain long-term project sustainability and improve user experience. Reducing technical debt prevents costly fixes later, while high customer satisfaction reflects the software's success in meeting user needs.
Top 5 metrics for Software Engineering Research
1. Defect Density
Defect density measures the number of defects confirmed in the software during a specific period of development divided by the size of the software.
What good looks like for this metric: Less than 1 defect per 1,000 lines of code
How to improve this metric:- Implement peer code reviews
- Conduct regular testing phases
- Adopt test-driven development
- Use static code analysis tools
- Enhance developer training programmes
2. Code Coverage
Code coverage is the percentage of your code which is tested by automated tests.
What good looks like for this metric: 80% - 90%
How to improve this metric:- Review untested code sections
- Invest in automated testing tools
- Aim for high test case quality
- Integrate continuous integration practices
- Regularly refactor and simplify code
3. Cycle Time
Cycle time measures the time from when work begins on a feature until it's released to production.
What good looks like for this metric: 1 - 5 days
How to improve this metric:- Streamline build processes
- Improve collaboration tools
- Enhance team communication rituals
- Limit work in progress (WIP)
- Automate repetitive tasks
4. Technical Debt
Technical debt represents the implied cost of future rework caused by choosing an easy solution now instead of a better approach.
What good looks like for this metric: Under 5% of total project cost
How to improve this metric:- Regularly refactor existing code
- Set priority levels for debt reduction
- Maintain comprehensive documentation
- Conduct technical debt assessments
- Encourage practices to avoid accumulating debt
5. Customer Satisfaction
Customer satisfaction measures the level of contentment clients feel with the software, often gauged through surveys.
What good looks like for this metric: Above 80% satisfaction rate
How to improve this metric:- Gather feedback through surveys
- Implement a user-centric design approach
- Enhance customer support services
- Ensure frequent updates and improvements
- Analyse and respond to customer complaints
How to track Software Engineering Research metrics
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your metrics.