Assessment and rating of sprints
This section explains my way of evaluating sprints in most of my project-based teachings. |
A TA evaluates each Sprint (one per week, most of the time). The evaluation will address five or six criteria and will take the following form:
![spiderJS](images/spiderJS.png)
These artifacts come from a course on software quality from my colleague Xavier Blanc (https://github.com/xblanc33/QualiteDev). |
Sorry for the French in the linked explanations. |
![profiles](images/profiles.png)
User values
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The way the goals have been captured (more details here)
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Evaluated by the Product Owner
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Should never decrease
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Sometimes no, or low, added value (Spikes, refactoring sprints)
US/Tasks
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The way Goals, US, and tasks are linked and traced here)
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Very tool-dependent (e.g., blocking issues, task lists)
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Can (should?) reach a high level pretty early
Documentation
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Technical and user documentations (more details here)
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As much automated as possible (javadoc,
.md
/.adoc
, code included rather than copy-pasted)
Tests/Quality
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How well are supported/explained the verification activities (more details here)
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Address and differentiate unit tests and integration tests
Build/CI/Release
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How professional and automated are the build, automated testing, deploy (more details here)
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Can (should?) reach a high level pretty early
Project typical evaluation sheet
Here is a typical scale:
Criterion | % |
---|---|
Respect for the Scrum method |
20% |
"Professional" character of dev |
20% |
Successive deliveries |
20% |
Tests / Documentations / Readme / wiki |
20% |
Code and application quality |
10% |
Final Customer Satisfaction |
10% |
I advise you to add such a table in your readme and self-evaluate your project. |
Useful tips
Technical Debt
Software Engineering term for Procrastination!
![techDebt](images/techDebt.png)
Commit messages
Have the same policy in the project:
[Fix|Feature|…] [Issue_Number]: Use a sentence with a capital letter and verb for the first word.
![commitMessagesIcons](images/commitMessagesIcons.png)
Useful links
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General
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The materials for the course: http://bit.ly/jmb-teaching
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The initial course about quality development: https://github.com/xblanc33/QualiteDev
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Python
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Defect prediction https://github.com/awsm-research/PyExplainer
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