The Pesticide Paradox in Software Testing

There ’ s an interesting term in husbandry called & nbsp;the pesticide paradox. When a pesky insect infestation is threatening a farmer ’ s hard-earned harvesting, he sprays the crop with pesticide, killing (most of) them in an instant. What a glorious win of man against nature! But there ’ s a haul: the midget percentage of those insect which fortunately survive will develop a natural resistance to the pesticide. & nbsp;
 

Now that they have less competition, they thrive, reproduce, and pass on their (potent) genes to the succeeding contemporaries. The more pesticide the sodbuster sprays, the more those insects adapt and become difficult to contain over clip. Eventually, the pesticide becomes ineffective, and the farmer has to resort to potent chemical that result in severe environmental harm.
 

The like pesticide paradox happens in software testing.

What is the Pesticide Paradox in Software Testing?

The Pesticide Paradoxin software testing was first introduced by & nbsp;Boris Beizer, a notable build in the field of software engineering, in his book & nbsp;`` Software Testing Techniques '', published in the 1980s. & nbsp;

The idea is that repeating the same exam causa over clip results in the software `` adapting, '' where defect become less potential to be observe because the tryout no longer cover new or acquire areas of the application. As software is updated—through new features, bug fixes, or refactoring—test cases that be erst effective may no longer catch fresh introduced bugs.

Causes of the Pesticide Paradox

The pesticide paradox gained grip in the testing community as software complexness grew, particularly during the ascension of automated testing in the late 1990s and 2000s. When test automation became more common, QA teams noticed that running the same machine-controlled test repeatedly did not perpetually uncover new defect.
 

There are 2 primary reasons for this:

1. Automation test scripts don ’ t catch new bug, they assure for bugs.

Let ’ s look at this Venn diagram below to have a better discernment.
 

Old and subsist bugs fall within the Assumptions/Requirements/Questions subdivision of our consciousness. We & nbsp;know & nbsp;that there can very well be a bug in that specific package module because we have understand it before. & nbsp;
 

However, the & nbsp;existent & nbsp;bugs lie in the & nbsp;Unknown Unknowndomain: we don ’ t even cognize that we don ’ t know about their existence. Automated script are only designed to quiz specific scenarios, conditions, or inputs that the developers can foresee. If you are not cognizant of their existence, how can you publish a test script for it?
 

Imagine you are quiz The Sims 4 game. You know that a bug will hap at the lineament customization stage if the histrion inputs an unrealistically long username, so you write an automated test hand for it.
 

But what if a bug happens when the exploiter simultaneously tries to switch between worlds and save the game while a household is experiencing a fire? These are the edge cases that don ’ t happen that frequently, but when it befall, it usually convey disaster.
 

The point is: mechanization test cases (the pesticide) do wonderment at checking known bugs, but when it comes to the less explored parts of the coating, we need some manual examination to complement the mechanization testing blind spots.
 

2. A system-under-test continuously evolves, but a test cause is static.

Pro tip: Tools like SUSA can handle this autonomously — upload your app and get results without writing a single test script.

Even well-designed test cases can become less effective over clip if they don ’ t change. In today ’ s highly dynamical, fast-paced, and ever-changing tech landscape, abide the like is just asking for obsolescence.
 

This makes signified, since package must continuously grow with new features append, while an automated examination event is merely create exclusively for a specific codebase at a specific point in time. When that codebase (i.e. the code that cause the bug) changes, the test script (i.e. the pesticide) must be updated to reflect the changes.
 

Strategies To Overcome The Pesticide Paradox

1. Have a ok balance between automation try and manual testing 

Automation testing is awesome with & nbsp;. However, it is unrealistic to swear solely on automation examination. Do not put all your eggs in one handbasket. When you adopt a hybrid coming of mechanization testing and manual testing, you get the best of both worlds.
 

How to do that? Use manual exploratory prove to discover new bug. These are the more subtle bugs that usually occupy 3+ interactions with the system to trigger. It is in the succeeding sprint/release that you write automated scripts for those glitch.
 

2. Regular test suite update for relevancy

Over clip, when new functionalities are added, UI is redesigned, and old features are removed, test cases must be updated accordingly. Running disused exam wastes resources without bestow any extra value, so don ’ t reprocess the same tests over and over. Instead, update them regularly.
 

After each release cycle, it is a recommended exercise to step back and name redundant tests. If a test no longer serves a role (e.g. tests covering deprecated features), it should be take. Similarly, if a characteristic is updated, the tests covering that feature should also be checked to ensure that they still align with the current software behavior.
 

3. Incorporate CI/CD

In a CI/CD pipeline, every code alteration, whether it 's a new feature, bug fix, or refactor, is integrate into the main codebase regularly, often several times a day. Thanks to this, outdated tests are flagged when they fail due to recent code changes, advertise the team to update or supplant them.
 

A well-configured CI/CD line can besides automatically run fixation tests on every habitus. This ensures that new code does not break existing functionality. Such an instant feedback loop keeps your test suite active and relevant. & nbsp;
 

4. Make your test design modular

You need a foot to make test upkeep leisurely. Enter modular test design.
 

Modular test design intend breaking your software down into modest, reusable units and create separate test cases for each of those unit. It promotes reusability, scalability, and maintainability because:

1. When application alteration occur (e.g., button renaming or variety structure changes), only a small subdivision of the test involve to be updated.
2. Modular functions like login operation or form filling can be reused across multiple tryout cases.
3. Adding new tests is leisurely as you can combine subsist components to cover different scenarios without compose new tests from scratch.

Let ’ s look at an example of how that is done in & nbsp;. In Katalon, you have an Object Repository that shop all of the test object that you can reuse across test environments. You can perform a Record-and-Playback by execute the tests as you would manually, and Katalon helps you turn all of your steps into a full viable tryout script that you can run on the surroundings of your choice.