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Top 10 Best Practices for Software Testing in 2024

Updated on Feb 22
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Detecting software bugs before releasing is crucial for ensuring project success from both user engagement and financial perspectives. The cost of solving bugs in the testing stage is almost seven times cheaper compared to the production stage. However, testing is time-consuming and expensive. Understanding the best practices in software testing can help QA specialists and executives to make better decisions that can lead to a more effective testing process. Therefore, in this article, we introduce the top 9 best software testing practices.

1- Planning

Testing should have a formal plan that guides all related parties and create a clear roadmap for testing. It should be well documented to establish clear communication. The goals and objectives of the plan should be SMART( specific, measurable, achievable, relevant, and time-bound). 

  • Specific: Define goals as specific and as narrow as possible.
  • Measurable: Define measurement tools and metrics that can track the progress
  • Achievable: Goals should be reasonably achievable in the given deadline
  • Relevant: Goals should be aligned with the overall business or project strategy
  • Time-bound: Set a deadline

2- Integrate testing in the development stage

Testing after the development stage is finished is not the best way. Testing should be integrated from the development stage and continue; this is called the shift-left strategy. Shift left testing has the following benefits:

  • Revealing bugs in an earlier stage will reduce the costs
  • Aligned with agile development practice
  • Increase coverage

To have successful integration, you should test early and often. Additionally, automating testing can have significant positive impacts. 

Automate testing

Reduction of testing time and improving testing coverage, accuracy and feedback speed are all possible with automation testing. Automation augments your workforce by giving repetitive tasks to bots. Thus, it enhances worker satisfaction since they can engage with more sophisticated tasks. Therefore, AIMultiple suggests automating the testing process as much as possible.

Sponsored

Numerous Fortune 500 organizations, including Nokia, Amazon, and BMW, trust Testifi as a provider of test automation tools. Their CAST & PULSE solutions, which have tracking and a real-time performance dashboard, offer web & API testing capabilities.

To understand more about automation testing, read Automation Testing: Types, Frameworks, Tools & Best Practices

To pick the right test automation tool, read Top 10 Test Automation Tools for 2023: A Detailed Benchmark 

3- Use test-oriented development practices

Test-oriented development practices can reduce the number of bugs and problems found in the testing stage because development is happening with a testing mindset. Practices include

Pair programming 

In this approach, two programmers work with a single computer. One writes code, and the other one observes and makes suggestions. Developers have to discuss, evaluate and decide on problems and trade-offs. This improves knowledge sharing and produces higher quality code as mistakes or bugs are caught before or during coding.

Test-driven development(TDD)

In this approach, first, the test is created, which initially fails. After that, the developer writes the code for the program, and after the code passes the test, the developer refactors the code (See Figure 1).

Figure 1: Test-driven development cycle 

Test-driven development cycle
Source: Woman on rails 

4- Adequate reporting of testing results 

Testing must be well documented. All the observations and progress of the test must be documented and incorporated into the testing report accordingly. The test report should be clear and not open to misinterpretation. They should include the following:

  • Bug recreation steps 
  • Screenshots or screen videos of the bugs, if possible, 
  • A clear description of how the functions should behave
  • Possible solutions for fixing the bugs.

5- Comprehensive testing coverage 

An application or an API has many different dimensions. Ensure that your tests cover all sizes; otherwise, you might miss problems and bugs. Do not focus on the functionality part only. UI/UX, performance, and security issues are examples of different dimensions that can have a profound impact on your success.  Thus, ensure they are tested. 

6- Test on real devices

Although simulation is a tempting option in testing, no simulator or emulator can reflect the actual end-user experience. The system, application, or bot automation tests need to be run in environments that encounter low batteries, slow internet, and network connections, pop-ups, etc. Testing on real devices enables developers to spot errors and fix them before launching the system.

7- Testing metrics practice

Measurement metrics must be utilized to monitor and measure the results and impacts of tests. To utilize testing metrics, you should: 

  • Choose the right metrics. There are many metrics, but before choosing them, ask :
  • Does it align with the business goal
  • Is it trackable, given our current infrastructure?
  • Set realistic targets for each metric. Achieving 100% might not be possible for most metrics, so identifying an achievable target is essential. 
  • Use metrics in tandem with each other. No single Metric has the power to explain everything. 
  • Collect comments and improve  KPIs. Ask the people who deal with metrics about their thoughts and comments; there is a chance that they have found shortcomings or have an idea for improving them. 
  • Make sure all related parties understand this and follow the cycle.

For more on testing metrics, read Top 9 Metrics That Measure Your Software Testing Efficiency in 2022. 

8- Distributing tasks according to skills

Although communication and collaboration are critical to a successful testing process, team members can have different skills that target specific automation testing roles. For example, writing test scripts requires QA testers with expert knowledge of scripting languages. In contrast, keyword-driven tests, which rely on simulating keyboard strokes and click buttons, can be prepared by non-technical team members.

9- Testing one feature per case

Writing a test case for independent features enables the reusability of the case in multiple tests or integration tests. Furthermore, it allows developers to understand errors in specific features instead of whole systems.

10- Provide version call option

Testing can fail to detect bugs which can lead to buggy updates that can break or change some functionality of an existing API. Version calling allows customers to use the old version. This minimizes the effect of bad updates on the API users.

Further Reading

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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