The Pesticide Paradox simply states that static test cases become stale and unproductive over time. The article above goes into tactical test methods of changing up the test cases to account for that affect and continue to find new defects. My contention in the comment was that the Pesticide Paradox can have some deep and subtle implications that should be considered when creating these new test case variations. Here, I elaborate on some of those considerations.
The Relationship of the Pesticide Paradox to TrainingOne may start correlating defects found with test cases and find that only new test cases produce defects. One reason for that could be training. In a quick, session-based, exploratory test sessions, the tester comes to rely on recognition of defect patterns that they are familiar with in those test cases that are already documented. Likewise, developers begin to identify those practices that produced those defects. This can be thought of as a form of cognitive bias, where the creation of new test cases serves to train the tester in different bug patterns. This continual establishment of new and different patterns is one reason that session-based testing is so effective when combined with a risk-based test area identification framework.
Possible Relationships Between Multiple ClustersEach defect cluster may identify a specific flaw in the code development. If the coders didn't identify the root cause of the flaws and change the practices, then these clusters may occur many times in different places. Was there a pattern to those clusters? This is can be thought of as a form of model-based testing, where we are looking for the underlying cause of the clusters. Superficially zeroing in on clusters without this overall view of the cluster patterns or modeling of underlying cause of the clusters would have limited value in improving test quality.
Layering of Defect ClustersThe clustering may be layered in complexity. New test cases may extend the previous tests down one layer without addressing the root cause of the defects. Example: A tester submits a set of defects one iteration where the GUI doesn't identify string overflows to the user. That is fixed with GUI field checks in all of the identified places. Next iteration, a middle-tier defect is found when an long string is entered in a field not addressed. Then the next iteration, database errors in the log are found that are due to other fields that are not addressed. Instead, the system architecture as a whole should have been evaluated initially to identify the extent of the problem.
These are just a few of the potential complexities that defect clustering can create. Actively questioning the underlying reasons for the clusters is always necessary to insure that the cause of the defects is addressed.