Saeed Rezaee

[SaeedRe]

The app is implemented in MVVM architectural pattern. The Koin library is used for dependency injection. The json data file is stored in assets folder.
The file is read in data module.
The core logic of the application is implemented in MainViewModel.kt file where data is fetch from the data module and the passed down to the view by data binding. The MainActivity.kt only shows the data and starts the process of the MainViewModel.kt The json file is read and converted into PaymentDetails.kt and PaymentModel.kt files. Some animations are implemented in the app and there is Animations.kt to handle the animations easily. The BindingAdapters.kt file contains the binding adapters for the activity_main.xml layout file to populate the data that are observed from the MainViewModel.kt

[pull-request-quantifier-deprecated]

This PR has 1248 quantified lines of changes. In general, a change size of upto 200 lines is ideal for the best PR experience!

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Quantification details

Label      : Extra Large
Size       : +1248 -0
Percentile : 100%

Total files changed: 41

Change summary by file extension:
.gitignore : +20 -0
.name : +1 -0
.gradle : +67 -0
.pro : +18 -0
.json : +42 -0
.xml : +468 -0
.kt : +375 -0
.properties : +30 -0
.bat : +68 -0
gradlew : +159 -0

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
  of this PR in relation to all other PRs within the repository.

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[SaeedRe]

Worked on adding online features to the app on my spare times.
Just for fun :)