Software bugs are an inevitable part of the SDLC (Software Development Life Cycle). No code is perfectly crafted from the get-go. Building a good software product calls for comprehensive testing and optimizations for bugs, anomalies, and errors to be identified, recorded, and resolved. To make the detection and prioritization of these bugs more efficient, the team must apply rigorous classification techniques to them.
While there are different types of bugs, in this article we will focus on the logical bugs and how teams can catch every one of them before production.
What are Logical Issues and Why are They the Hardest to Catch?
A logical issue causes the software to function incorrectly and disrupts its intended workflow but does not terminate abnormally. These flaws can cause unexpected software behaviour, including crashes. Poorly written code or misinterpretation of business logic are the most common causes of logical bugs.
The infinite loop is a common example of logical bugs where the programme repeats a sequence indefinitely due to badly designed code until it crashes or halts due to external action, such as the user closing a browser window or turning off the power. Some other examples are:
Assigning the wrong value to a variable
Multiplying two numbers instead of adding them
APIs carry the majority (i.e. more than 80%) of all the functional and business logic of all modern apps. Functions like the ability to apply a discount on an eCommerce app, calculate premium accurately for a policy inside an insurance app or sort stocks based on market prices for a stockbroking app, are core business logics that set expected user behaviours.
But much of what we take for granted can stop working. For example, when we can’t apply the discount, or apply an incorrect discount, seeing a higher premium for my policy etc, this happens because of an unwanted code change, and that in turn changes the way the existing API(s) respond. Worst case scenario, the teams testing for functional or logical issues can never potentially know at the time of testing, what to look for and where, and as a result, undesired changes leak to actual users.
How Does HyperTest Catch Every Undesired Change Without Writing a Single Line of Code?
For HyperTest, the stable version of any app that is currently live with users, happens to be the source of truth.
Any change that damages any of the app functions or logic naturally has to be a deviation from the stable version that determines the current baseline behaviour. So the best way to detect an unwanted change is to compare the new test version with the stable/baseline version for all parameters in the API response and report any deviations that surface.
Now, such a comparison could flag completely benign changes. For instance, dynamic keys change value with consecutive API responses, but these can be identified and filtered out, leaving only those deviations that are truly breaking in nature.
Now, a way to catch these changes early in the dev cycle is to run these comparisons automatically when a new pull request PR is created. The comparisons take less than a few mins and once reviewed by the dev who pushed the pull request PR, the request can then be merged to the master version.
This way agile teams can catch breaking changes early in the release cycle, very close to the dev, without worrying about missing critical logical issues that are super hard to catch and nearly impossible to write tests for.
If you’d like to see this in action for your app, schedule a set-up call with the automation team and hit the ground running in less than 45 mins.
Happy testing!