This blog reviews the article “Heuristics For Mushroom Picking (And Testing)“.
The article draws parallels between the practices of mushroom foraging and software testing, emphasizing the importance of preparation and contextual awareness. Just as successful mushroom pickers need to study and understand the environment where mushrooms thrive, testers must focus on identifying areas in software that are more likely to harbor bugs. Both processes involve observing patterns, analyzing risks, and carefully distinguishing between valuable finds and potentially harmful ones—highlighting how a methodical approach enhances success while minimizing errors.
Additionally, the article delves into the uncertainty inherent in both activities. Mushroom pickers often face the challenge of distinguishing edible species from toxic ones, akin to testers identifying false positives among suspected bugs. Both scenarios demand a balance of curiosity, caution, and decision-making skills under incomplete information. The comparison sheds light on how these heuristics can be applied to improve efficiency and reliability in software testing while also drawing lessons from the exploratory and thoughtful nature of mushroom foraging.
I think that this article does a good job at explaining the similarities of mushroom picking and software testing. I think it benefits from the comparison because it allows software testing to be explained in a simpler way, helping a larger amount of people understand the topic.
This blog reviews the article “Heuristics For Mushroom Picking (And Testing)“.
The article draws parallels between the practices of mushroom foraging and software testing, emphasizing the importance of preparation and contextual awareness. Just as successful mushroom pickers need to study and understand the environment where mushrooms thrive, testers must focus on identifying areas in software that are more likely to harbor bugs. Both processes involve observing patterns, analyzing risks, and carefully distinguishing between valuable finds and potentially harmful ones—highlighting how a methodical approach enhances success while minimizing errors.
Additionally, the article delves into the uncertainty inherent in both activities. Mushroom pickers often face the challenge of distinguishing edible species from toxic ones, akin to testers identifying false positives among suspected bugs. Both scenarios demand a balance of curiosity, caution, and decision-making skills under incomplete information. The comparison sheds light on how these heuristics can be applied to improve efficiency and reliability in software testing while also drawing lessons from the exploratory and thoughtful nature of mushroom foraging.
I think that this article does a good job at explaining the similarities of mushroom picking and software testing. I think it benefits from the comparison because it allows software testing to be explained in a simpler way, helping a larger amount of people understand the topic.
This blog reviews the article “Heuristics For Mushroom Picking (And Testing)“.
The article draws parallels between the practices of mushroom foraging and software testing, emphasizing the importance of preparation and contextual awareness. Just as successful mushroom pickers need to study and understand the environment where mushrooms thrive, testers must focus on identifying areas in software that are more likely to harbor bugs. Both processes involve observing patterns, analyzing risks, and carefully distinguishing between valuable finds and potentially harmful ones—highlighting how a methodical approach enhances success while minimizing errors.
Additionally, the article delves into the uncertainty inherent in both activities. Mushroom pickers often face the challenge of distinguishing edible species from toxic ones, akin to testers identifying false positives among suspected bugs. Both scenarios demand a balance of curiosity, caution, and decision-making skills under incomplete information. The comparison sheds light on how these heuristics can be applied to improve efficiency and reliability in software testing while also drawing lessons from the exploratory and thoughtful nature of mushroom foraging.
I think that this article does a good job at explaining the similarities of mushroom picking and software testing. I think it benefits from the comparison because it allows software testing to be explained in a simpler way, helping a larger amount of people understand the topic.
This blog reviews the article “Heuristics For Mushroom Picking (And Testing)“.
The article draws parallels between the practices of mushroom foraging and software testing, emphasizing the importance of preparation and contextual awareness. Just as successful mushroom pickers need to study and understand the environment where mushrooms thrive, testers must focus on identifying areas in software that are more likely to harbor bugs. Both processes involve observing patterns, analyzing risks, and carefully distinguishing between valuable finds and potentially harmful ones—highlighting how a methodical approach enhances success while minimizing errors.
Additionally, the article delves into the uncertainty inherent in both activities. Mushroom pickers often face the challenge of distinguishing edible species from toxic ones, akin to testers identifying false positives among suspected bugs. Both scenarios demand a balance of curiosity, caution, and decision-making skills under incomplete information. The comparison sheds light on how these heuristics can be applied to improve efficiency and reliability in software testing while also drawing lessons from the exploratory and thoughtful nature of mushroom foraging.
I think that this article does a good job at explaining the similarities of mushroom picking and software testing. I think it benefits from the comparison because it allows software testing to be explained in a simpler way, helping a larger amount of people understand the topic.
This week in class we learned about path testing, which is a white box method that examines code to find all possible paths. Path testing uses control flow graphs in order to illustrate the different paths that could be executed in a program. In the graph, the nodes represent the lines of code, and the edges represent the order in which the code is executed. Path testing appealed to me as a testing method because it gives visual representations of how the source code should execute given different inputs. I took a deeper dive into path testing after this week’s classes and found this blog that gave me a deeper understanding of path testing.
Steps
When you have decided that you want to perform path testing, you must create a control flow graph that matches up with the source code. For example, the split of direction between nodes should represent if-else statements and for while loops, the nodes towards the end of the program that have an edge pointed at an earlier node.
Secondly, pick out a baseline path for the program. This is the path you define to be the original path of your program. After the baseline is created, continue generating paths representing all possible outcomes in the execution.
How many Test Cases?
For a lengthy source code, the possible outcomes could seem endless and could therefore end up being a difficult, time-consuming task to do manually. Luckily, there is an equation that determines how many test cases a program will need with path testing.
C = E – N + 2P
Where C stands for cyclomatic complexity. The cyclomatic complexity is equivalent to the number of linearly independent paths, which in turn equals the number of required test cases. E represents the number of edges, N is the number of nodes, and P is the number of connected components. Note that for a single program or source of code, P = 1 always.
Benefits
Path testing reveals outcomes that otherwise may not have been known without examining the code. As stated before, it can be difficult for a tester to know all the possible outcomes in a class. Path testing provides a solution to that by using control flow charts, where the tester can examine the different paths. Path testing also ensures branch coverage. Developers don’t need to merge code with an existing repository because the developers can test in their own branch. Unnecessary and overlapping tests are another thing developers don’t have to worry about.
Drawbacks
Path testing can also be time consuming. Quicker testing methods do exist and take less time off further developing projects. Also in many cases, path testing may be unnecessary. Path testing is used often by many DevOps setups that require a certain amount of unit coverage before deploying to the next environment. Outside of this, it may be considered inefficient compared to another testing method.
I chose this blog because I was interested in learning how to optimize the selection of test cases without sacrificing thorough coverage. During my search, I came across an article on TestGrid.io on equivalence partitioning testing, which was fantastic in removing redundancy from testing. As I develop my programming and software testing skills, I have found this method especially useful in making the testing process simpler.
Equivalence partitioning is a testing technique applied to partition input data into partitions or groups based on the assumption that values in each partition will behave similarly. Instead of testing each possible input, testers choose a sample value from each partition and hope the entire group will result in the same output. This reduces the number of test cases but still provides sufficient coverage.
For example, if a program is able to accept input values ranging from 1 to 100, equivalence partitioning allows the testers to categorize them into two sets: valid values (1-100) and invalid values (less than 1 or more than 100). Rather than testing every number in the valid set, a tester would choose sentinel values like 1, 50, and 100. Similarly, they would test the invalid range with 0 and 101. This is time-efficient but identifies errors simultaneously.
I employed the TestGrid.io article because it explains equivalence partitioning in an understandable and systematic manner. Much other testing material is too complex or ambiguous for newcomers, but this article simplifies it and incorporates real-world examples. This allowed it to be simple not only to understand the theory, but also to apply the method to real-life situations.The article also discusses the advantages of equivalence partitioning, including reducing redundant test cases, being more efficient, and offering complete coverage. As an individual interested in improving my testing methods, I found this useful because it corresponds with my goal of producing better, more efficient test cases without redundant repetition.
Equivalence partitioning testing is a sound approach to maximizing test case selection. It enables the tester to focus on representative cases rather than testing all possible inputs, which is time- and effort-efficient. The TestGrid.io article provided a clear understanding of how to implement this method and why it is significant. For me, learning effective test methods like equivalence partitioning will make me more efficient in my coding, debugging, and software developing abilities, prepared for internships, projects, and software engineering positions.
As this project is the first time I have worked in a team adopting the scrum methodology, I was a little hesitant at first as to how well we would work together, with possible conflicting problems that would only lead to decreased productivity, but the way that it has turned out isn’t like that at all. An important part of the lack of disagreements comes from our collective trust in each other to produce the outcome that we envisioned from the issue that they were assigned. Our issue allowed us to split our team into smaller teams, focusing on separate issues which would eventually connect to fulfill our team’s original project goal. This delegation of tasks to each subteam has allowed us to focus on our own tasks, while also checking in with each other to see how the rest are doing. This frees us of the burden of trying to work over each other to accomplish one thing which I believe would lead to inefficiencies and conflicting ideas. This subteam workflow allows us to dedicate our time and effort to one topic while checking in on the other topics to stay updated and give feedback.
Although I just mentioned that the subteam system allows us to check in on each other and give feedback, this is somewhat untrue or at the very least an idealized version of what we are practicing. I do feel that I am working well in my subteam, and with my partner we are efficiently approaching our goal, however when it comes time to check in on the other teams, I feel as though I don’t get enough of an idea of the inner workings of what they’re doing to give good, constructive feedback. The same goes for my teammates giving me feedback. I think that is the tradeoff with this system though because it entrusts you with knowing what you’re doing, and I don’t think it’s as detrimental as it could be because each subteam at least has a partner that does give detailed feedback, sacrificing quantity for quality.
Apprenticeship Pattern
The perpetual learning pattern assumes there is always more knowledge to be known and your journey as a learner is never truly over. I selected this pattern because I think the pursuit of knowledge is important to sustain a continued expansion of knowledge and innovation, as well as satisfying a person’s internal sense of accomplishment and fulfillment. The point that this book tries to make about being a software apprentice is that you can become a master within your lifetime, but that doesn’t mean you stop learning. This pattern fits into the sprint because there was never a time I really felt like I fully understood a concept to a point where there wasn’t anything else I could learn by further investigating it or digging deeper. Even in established technologies or languages like HTML or CSS, there are a lot of things that many people may not know and working with it so much means I am more comfortable with it, but it doesn’t mean I am anywhere near mastering it, following this pattern of perpetual learning.
If I had thought of this pattern before the sprint started I would have gone into it more open-minded, knowing that anything I encountered would be surface level and that if I really wanted to I could go down a rabbit hole for each new thing I learned. There’s so much information out there that is almost impossible to get to, especially for me in the time of one sprint. I think to improve as an individual in future sprints, I could go out of my way to learn as much as I can so I can understand what I’m working on as best I can.
This week, our class learned about path testing. It is a white box testing method (meaning we do not actually run the source code) that tests to make sure every specification is met and helps to create an outline for writing test cases. Each line of code is represented by a node and each progression point the compiler follows is represented by a directed edge. Test cases are written for each possible path the compiler can take.
For example, if we are testing the code block below, a path testing diagram can be created: also shown below. Each node represents a line of code and each directed edge represents where the compiler goes next, depending on the conditions. Test cases are written for each condition: running through the while loop and leaving when value is more than 5 or bypassing it if value starts as more than 5.
I wanted to learn more about path testing, so I did some research and found a blog that mentioned cyclomatic complexity. Cyclomatic complexity is a number that classifies how complex the code is based on how many nodes, edges, and conditionals you have. This number will relate to how many tests you need to run, but is not always the same number. The cyclomatic complexity of the example above would be (5-5)+2(1) = 2.
The blog also explores the advantages and disadvantages of path based testing. Some advantages are performing thorough testing of all paths, testing for errors in loops and branching points, and ensuring any potential bugs in pathing are found. Some disadvantages are failing to test input conditions or runtime compilations, a lot of tests need to be written to test every edge and path, and exponential growth in the number of test cases when code is more complex.
Another exercise we did in class was condensing nodes that do not branch. In the example above, node 2 and 3 can be condensed into one node. This is because there is no alternative path that can be taken between the nodes. If line 2 is run, line 3 is always run right after, no matter what number value is. Condensing nodes would be helpful in slightly more complex programs to make the diagram more readable. Though if you are working with a program with a couple hundred lines, this seems negligible.
When I am writing tests for a program in the future, I would probably choose a more time conscious method. Cyclomatic complexity is a good and useful metric to have, but basing test cases off of the path testing diagram does not seem practical for complex codes and tight time constraints.
If you’re like me, getting into software testing might feel overwhelming at first. There are countless methods and techniques, each with its own purpose, and it’s easy to feel lost. But when I first learned about Equivalence Class Testing, something clicked for me. It’s a simple and efficient way to group similar test cases, and once you get the hang of it, you’ll see how much time and effort it can save.
So, what exactly is Equivalence Class Testing? Essentially, it’s a method that helps us divide the input data for a program into different categories, or equivalence classes, that are expected to behave the same way when tested. Instead of testing every single possible input value, you select one or two values from each class that represent the rest. It’s like saying, “if this value works, the others in this group will probably work too.” This approach helps you avoid redundancy and keeps your testing efficient and focused.
Now, why should you care about Equivalence Class Testing? Well, let me give you an example. Imagine you’re writing a program that processes numbers between 1 and 1000. It would be impossible (and impractical!) to test all 1000 values, right? With Equivalence Class Testing, you can group the numbers into a few categories, like numbers in the lower range (1-200), the middle range (201-800), and the upper range (801-1000). You then pick one or two values from each group to test, confident that those values will tell you how the whole range behaves. It’s a major time-saver.
When I started using this method, I realized that testing every possible input isn’t just unnecessary—it’s counterproductive. Instead, I learned to focus on representative values, which allowed me to be much more efficient. For instance, let’s say you’re testing whether a student is eligible to graduate based on their GPA and number of credits. You could create equivalence classes for the GPA values: below 2.0, which would likely indicate the student isn’t ready to graduate; between 2.0 and 4.0, which might be acceptable; and anything outside the 0.0 to 4.0 range, which is invalid. Testing just one GPA value from each class will give you a pretty good sense of whether your function is working properly without overloading you with unnecessary cases.
Another thing I love about Equivalence Class Testing is that it naturally leads into both Normal and Robust Testing. Normal testing focuses on valid inputs—values that your program should accept and process correctly. Robust testing, on the other hand, checks how your program handles invalid inputs. For example, in our GPA scenario, testing GPAs like 2.5 or 3.8 would be normal testing, but testing values like -1 or 5 would fall under robust testing. Both are essential for making sure your program is strong and can handle anything users throw at it.
Lastly, when I first heard about Weak and Strong Equivalence Class Testing, I was a bit confused. But the difference is straightforward. Weak testing means you’re testing just one value from each equivalence class at a time for a single variable. On the other hand, Strong testing means you’re testing combinations of values from multiple equivalence classes for different variables. The more variables you have, the more comprehensive your tests will be, but it can also get more complex. I usually start with weak testing and move into strong testing when I need to be more thorough.
Overall, learning Equivalence Class Testing has made my approach to software testing more strategic and manageable. It’s a method that makes sense of the chaos and helps me feel more in control of my testing process. If you’re new to testing, or just looking for ways to make your tests more efficient, I highly recommend giving this method a try. You’ll save time, energy, and still get great results.
Hello everyone, and welcome back to my weekly blog post! This week, we’re diving into an essential software testing technique: Pairwise and Combinatorial Testing. These methods help testers create effective test cases without needing to check every single possible combination of inputs. Similar to most of the test cases selection methods we’ve learned.
To make things more relatable, let’s start with a real-life example from the insurance industry.
A Real-Life Problem: Insurance Policy Testing
Imagine you are working for an insurance company that sells car insurance. Customers can choose different policy options based on:
Car Type: Sedan, SUV, Truck
Driver’s Age: Under 25, 25–50, Over 50
Coverage Type: Basic, Standard, Premium
If we tested every possible combination, we would have:
3 × 3 × 3 = 27 test cases!
This is just for three factors. If we add more, such as driving history, location, or accident records, the number of test cases grows exponentially, making full testing impossible.
So how can we test efficiently while ensuring that all critical scenarios are covered? That’s where Pairwise and Combinatorial Testing come in!
What is Combinatorial Testing?
Combinatorial Testing is a technique that selects test cases based on different input combinations. Instead of testing all possible inputs, it chooses a smaller set that still covers key interactions between variables.
Example: Combinatorial Testing for Insurance Policies
Instead of testing all 27 cases, we can use combinatorial testing to reduce the number of test cases while still covering important interactions.
A possible set of test cases could be:
Test Case
Car Type
Driver’s Age
Coverage Type
1
Sedan
Under 25
Basic
2
SUV
25–50
Standard
3
Truck
Over 50
Premium
4
Sedan
25–50
Premium
5
SUV
Over 50
Basic
6
Truck
Under 25
Standard
This method reduces the number of test cases while ensuring that each factor appears in multiple meaningful combinations.
What is Pairwise Testing?
Pairwise Testing is a type of combinatorial testing where all possible pairs of input values are tested at least once. Research has shown that most defects in software are caused by the interaction of just two variables, so testing all pairs ensures good coverage with fewer test cases.
Example: Pairwise Testing for Insurance Policies
Instead of testing all combinations, we can create a smaller set where every pair of values appears at least once:
Test Case
Car Type
Driver’s Age
Coverage Type
1
Sedan
Under 25
Basic
2
Sedan
25–50
Standard
3
SUV
Under 25
Premium
4
SUV
Over 50
Basic
5
Truck
25–50
Premium
6
Truck
Over 50
Standard
Here, every pair (Car Type, Driver’s Age), (Car Type, Coverage Type), and (Driver’s Age, Coverage Type) appears at least once. This means we cover all important interactions with just 6 test cases instead of 27!
Permutations and Combinations in Testing
To understand combinatorial testing better, we need to understand permutations and combinations. These are ways of arranging or selecting elements from a set.
What is a Combination?
A combination is a selection of elements where order does not matter. The formula for combinations is: C(n, r) = n! / [r! * (n – r)!]
where:
n is the total number of items
r is the number of selected items
! (factorial) means multiplying all numbers down to 1
Example of Combination in Insurance
If an insurance company wants to offer 3 different discounts from a list of 5 available discounts, the number of ways to choose these discounts is: C(5,3)=5!3!(5−3)!
So, there are 10 different ways to choose 3 discounts.
What is a Permutation?
A permutation is an arrangement of elements where order matters. The formula for permutations is: P(n, r) = n! / (n – r)!
where:
n is the total number of items
r is the number of selected items
Example of Permutation in Insurance
If an insurance company wants to assign 3 priority levels (High, Medium, Low) to 5 claims, the number of ways to arrange these claims is: P(5,3)=5!(5−3)!
So, there are 60 different ways to assign priority levels to 3 claims.
Why Use Pairwise and Combinatorial Testing?
Saves Time and Effort – Testing fewer cases while maintaining coverage.
Covers Critical Scenarios – Ensures every important combination is tested.
Finds Defects Faster – Most bugs are caused by two interacting factors, so pairwise testing helps detect them efficiently.
Reduces Costs – Fewer test cases mean lower testing costs and faster releases.
When Should You Use These Techniques?
When a system has many input variables
When full exhaustive testing is impractical
When you need to find bugs quickly with limited resources
When testing insurance, finance, healthcare, and other complex systems
Tools for Pairwise and Combinatorial Testing
To make the process easier, you can use tools like:
PICT (Pairwise Independent Combinatorial Testing Tool) – Free from Microsoft
Hexawise – A combinatorial test design tool
ACTS (Automated Combinatorial Testing for Software) – Developed by NIST
These tools help generate optimized test cases automatically based on pairwise and combinatorial principles.
Conclusion
Pairwise and Combinatorial Testing are powerful techniques that allow testers to find defects efficiently without having to test every possible combination. They save time, reduce costs, and improve software quality.
Next time you’re dealing with multiple input variables, try using Pairwise or Combinatorial Testing to make your testing smarter and more effective!
