Software Testing Series: Part 1 – Best Practices, Test Strategies, and Test Modeling

[By Limor Leah Wainstein]

Testing the software you develop is vital to ensuring customer satisfaction with an application and its reliability. However, merely testing software is not enough—you need to know how to test properly, which encompasses several important factors.

The following post is the first in a three-part series on software testing, including recommendations for how to do it effectively. Part one provides an introductory view of software testing best practices, strategy development, and test modeling. Future posts will highlight some software quality metrics, how to conduct the risk analysis, and more.

Software Testing Best Practices

While it’s true that each development team probably has different opinions on the best way to test software, it’s imperative not to drift too far from the prevailing best practices when testing.

Established best practices act as a signpost—they point you towards the right way to test the applications under development, minimizing the risk of defects escaping into production. Best practices are classed as such for a reason—it’s because they drive effective testing efforts.

Deviating too far from established and agreed upon ways to do things carries with it a greater risk of something going wrong with software tests, such as missing important errors that make it through to the customer.

Some examples of software testing best practices include:

  • Testing throughout the software development cycle
  • Writing tests for maximum coverage—even if 100 percent is not possible, always try to maximize how much of the application and its behavior you test
  • Keeping detailed notes on all tests to ensure unambiguous test reports
  • Automating all possible tests that you can automate

Following the best practices for software testing doesn’t guarantee an effective testing effort, but it undeniably decreases the risk of something going wrong.

Use a Solid Software Testing Strategy

A test strategy is used to outline the testing approach that a company takes when testing its software. The test strategy includes details on the testing objectives, time and resources required for testing, the testing environment, and descriptions of the types of testing planned.

Quite apart from developing software to meet particular customer functional requirements, all applications must also run smoothly under a variety of conditions. Performance risk relates to the risk that the application doesn’t function because of memory issues, changes in how customers use the software, code changes, or other potential performance bottlenecks.

The standard method for testing software to identify performance risk is to simulate different production loads and check how the software copes with strenuous use. Load testing is often conducted at the end of the development phase when bugs are more costly to fix.

A potential benefit of designing a solid test strategy from the outset can shift from a reactive performance testing approach to a proactive performance engineering approach.

Everyone takes part in performance engineering—it occurs throughout the development cycle. Designing a solid test strategy leads to risk-based testing, in which all performance risks related to a software release are identified during test planning, and a risk mitigation plan is drawn up. The software can thus be developed to minimize those performance risks from causing issues later on in the development cycle.

Load testing still plays an important role in verifying the functioning of the application under specific loads, but the point is that the right test strategy can ensure applications are more likely to pass their performance tests because potential performance issues have been dealt with earlier.

Model Your Tests

It’s imperative to create functional test cases that attempt to validate the realistic behavior of the system under development. Testing teams can achieve this by creating a model of the system that needs testing. The model is an abstraction—a partial representation of the system’s desired behavior derived from the software requirements. A set of realistic test cases can then be generated from models automatically using model-based testing (MBT) tools such as fMBT or TestOptimal.

Modeling ensures realistic test cases can be generated. An additional advantage of using a model is lower project maintenance because it is easier with a model re-generate test cases for new features.


  • Good software testing depends firstly on a firm understanding of established best practices for testing software. While each team has its perspectives on how to test, it helps not to deviate too far from these best practices.
  • Implementing a solid test strategy helps with identifying key performance risks and building software with those risks in mind. Performance testing is, therefore, less likely to become a bottleneck because teams have dealt with risks earlier in development.
  • Creating a model of the system under development helps teams create realistic test cases. Furthermore, a model requires less maintenance later on when test cases need to be made again to test new software features.

About Limor Leah Wainstein:

Limor is a technical writer and editor at focused on technology and SaaS markets. She began working in Agile teams over ten years ago and has been writing technical articles and documentation ever since. She writes for various audiences, including on-site technical content, software documentation, and dev guides. She specializes in big data analytics, computer/network security, middleware, software development and APIs.

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