Book Image

Hands-On Software Engineering with Python

By : Brian Allbee, Nimesh Verma
Book Image

Hands-On Software Engineering with Python

By: Brian Allbee, Nimesh Verma

Overview of this book

Software Engineering is about more than just writing code—it includes a host of soft skills that apply to almost any development effort, no matter what the language, development methodology, or scope of the project. Being a senior developer all but requires awareness of how those skills, along with their expected technical counterparts, mesh together through a project's life cycle. This book walks you through that discovery by going over the entire life cycle of a multi-tier system and its related software projects. You'll see what happens before any development takes place, and what impact the decisions and designs made at each step have on the development process. The development of the entire project, over the course of several iterations based on real-world Agile iterations, will be executed, sometimes starting from nothing, in one of the fastest growing languages in the world—Python. Application of practices in Python will be laid out, along with a number of Python-specific capabilities that are often overlooked. Finally, the book will implement a high-performance computing solution, from first principles through complete foundation.

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Hands-On Software Engineering with Python
Brian Allbee | Nimesh Verma
Oct, 2018 | 736 pages
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Table of Contents (21 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
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1
Programming versus Software Engineering
Programming versus Software Engineering
The bigger picture
Asking questions
Summary
2
The Software Development Life Cycle
The Software Development Life Cycle
Pre-development phases of the SDLC
Development – specific phases of the SDLC
Post-development phases of the SDLC
Summary
3
System Modeling
System Modeling
Architecture, both logical and physical
Use cases (business processes and rules)
Data structure and flow
Interprocess communication
System scope and scale
Summary
4
Methodologies, Paradigms, and Practices
Methodologies, Paradigms, and Practices
Process methodologies
Development paradigms
Development practices
Summary
5
The hms_sys System Project
The hms_sys System Project
Goals for the system
What's known/designed before development starts
What the iteration chapters will look like
Summary
6
Development Tools and Best Practices
Development Tools and Best Practices
Development tools
Best practices
Summary
7
Setting Up Projects and Processes
Setting Up Projects and Processes
Iteration goals
Assembly of stories and tasks
Setting Up SCM
Stubbing out component projects
Integrating tests with the build process
Summary
8
Creating Business Objects
Creating Business Objects
Iteration goals
Assembly of stories and tasks
A quick review of classes
Implementing the basic business objects in hms_sys
Summary
9
Testing Business Objects
Testing Business Objects
Starting the unit testing process
Unit testing patterns established so far
Distribution and installation considerations
Quality assurance and acceptance
Operation/use, maintenance, and decommissioning considerations
Summary
10
Thinking About Business Object Data Persistence
Thinking About Business Object Data Persistence
Iterations are (somewhat) flexible
Data storage options
Selecting a data storage option
Polymorphism (and programming to an interface)
Data access design strategies
Summary
11
Data Persistence and BaseDataObject
Data Persistence and BaseDataObject
The BaseDataObject ABC
Unit testing BaseDataObject
Summary
12
Persisting Object Data to Files
Persisting Object Data to Files
Setting up the hms_artisan project
Creating a local file system data store
Implementing JSONFileDataObject
The concrete business objects of hms_artisan
Summary
13
Persisting Data to a Database
Persisting Data to a Database
The Artisan Gateway and Central Office application objects
The concrete business objects of the Central Office projects
Summary
14
Testing Data Persistence
Testing Data Persistence
Writing the unit tests
Testing hms_artisan.data_storage
Testing the new hms_core Classes
Unit tests and trust
Building/distribution, demonstration, and acceptance
Operations/use, maintenance, and decommissioning considerations
Summary
15
Anatomy of a Service
Anatomy of a Service
What is a service?
Service structure
A generic service design
Integrating a service with the OS
Summary
16
The Artisan Gateway Service
The Artisan Gateway Service
Overview and goal
Iteration stories
Messages
Deciding on a message-transmission mechanism
Traffic to and from the service
Impacts on testing and deployment
Summary
17
Handling Service Transactions
Handling Service Transactions
Remaining stories
A bit of reorganization
Preparation for object transactions
Product object transactions
Artisan object transactions
Order object transactions
When do messages get sent?
Summary
18
Testing and Deploying Services
Testing and Deploying Services
The challenges of testing services
The overall testing strategy
Demonstrating the service
Packaging and deploying the service
Where hms_sys development could go from here
Summary
19
Multiprocessing and HPC in Python
Multiprocessing and HPC in Python
Common factors to consider
A simple but expensive algorithm
Local parallel processing
Parallelizing across multiple machines
Integrating Python with large-scale, cluster computing frameworks
Summary 
20
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Data storage options

All of the options that will be given serious consideration have a few common properties:

  • They will allow data to be stored offline, so that the application or service program doesn't need to be running constantly in order to ensure that the relevant data isn't lost
  • They have to allow the applications and service to perform at least three of the four standard CRUD operations:
    • Create: Allowing data for new objects to be stored.
    • Read: Allowing access to data for existing objects, one at a time, all at once, and possibly with some filtering/searching capabilities.
    • Update: Allowing existing data to be altered when/if needed.
    • Delete: Allowing (perhaps) the ability to remove data for objects that are no longer relevant. At a minimum, flagging such data so that it's not generally available will work as well.

They should also be...

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