Skip to main content

How To achieve 100% test coverage in pytest





Write Additional Tests: Write new test cases to cover the remaining lines of code. This might involve creating edge cases, boundary conditions, or scenarios that are not currently covered by your tests.


Mock External Dependencies: If your code interacts with external dependencies such as databases, APIs, or file systems, use mocking to simulate these dependencies in your tests. This ensures that you can cover all the code paths without relying on the external environment.


Refactor Code:  Sometimes, achieving full test coverage might require refactoring your code to make it more testable. This could involve breaking down large functions into smaller units, reducing dependencies between components, or making your code more modular.



Comments

Popular posts from this blog

Implementing Advance Query Optimization in Django ORM

 Django's ORM makes database interactions seamless, allowing developers to write queries in Python without raw SQL. However, as applications scale, inefficient queries can slow down performance, leading to high latency and database load.  This guide explores advanced query optimization techniques in Django ORM to go beyond basic CRUD (Create, Read, Update, Delete) operations and improve efficiency.  1. Use QuerySet Caching to Avoid Repeated Queries Using cache reduces redundant queries for frequently accessed data. Caching helps reduce repeated database hits. 2. Avoid .count() on Large Datasets Using .count() on large tables can be expensive Inefficient way: Optimized way ( .exists() is Faster) 3. Use Indexes for Faster Lookups Indexes speed up queries on frequently filtered fields. Add db_index=True for frequently queried fields: 4. Optimize Bulk Inserts and Updated Performing operations on multiple records one by one is inefficient. Use bulk_create() for mass insert...

Database Indexing in Django application

  Database Indexing Database indexing is a technique used to optimize the performance of database queries by allowing the database management system (DBMS) to quickly locate and retrieve specific rows of data. Indexes are data structures that provide a faster way to look up records based on the values stored in one or more columns of a table. When you create an index on a table, the DBMS creates a separate data structure that maps the values in the indexed columns to the corresponding rows in the table. Default Type of Index is B-Tree Index ( The king of all indexes) বইতে কোন টপিক খুজতে গেলে আমরা টেবিল অফ কনটেন্ট থেকে দেখি এই টপিক কত নম্বর পেজে আছে।যাতে করে আমাদের পুরো বই খুজতে না হয়। ডেটাবেজ ইনডেক্সিং ও তেমনই একটা ইফিসিয়েন্ট টেকনিক।ডেটাবেজে কোন ডেটাকে দ্রুত খুজে বের করার জন্য ইনডেক্সিং করা লাগে।যদি এমন হয় একটা কুয়েরি বার বার এক্সিকিউট করতে হচ্ছে এবং একটা কলাম থেকে ভ্যালু বার বার খুজতে হচ্ছে তখন আমরা সেই কলামে ইনডেক্সিং করতে পারি।এর মাধ্যমে কোন ডেটা দ্রুত রিট্রাইভ করা যায়।কিন্তু ই...

Django select_related and prefetch_related

  Difference between select_related and prefetch_related Reducing SQL queries is one of the first steps when optimizing a Django project. There are two powerful methods included in the Django ORM to help us that can boost performance by creating a single more complex QuerySet rather than multiple, smaller queries. In this project we will understand about  select_related and prefetch_related.  Django use these two orm method to reduce sql queries in database based on different scenario.  select_related Lets assume  this two model we have.  class Author ( models . Model ): name = models . CharField ( max_length = 200 ) def __str__ ( self ): return self . name class Courses ( models . Model ): name = models . CharField ( max_length = 200 ) author = models . ForeignKey ( Author , on_delete = models . CASCADE , related_name = 'courses' ) def __str__ ( self ): return self . name Here we have two mode. ...