What are some strategies for testing edge cases and exceptions in algorithms?

Having detailed requirement specification(marketing), functional specification(r&d) and test-plan(qa) is key. Unit testing and using coverage results as feedback and random stimulus when needed. Alpha and beta customer engagements provide realistic scenarios and help find problems early.

Testing edge cases is especially difficult but very important for machine learning powered applications as these edges cases could come from not only the model bugs (algorithmic implementation issues), but also from data changes (concept drift and data shift). That's why ML engineering in production requires monitoring of both data and model behavior to avoid silent failures.

In terms of those preparing for big tech companies DSA test, testing edge cases basically comes from assumption that your technical recruiter gives you. When working on test cases a good understanding of your base programming language is very important and if you understand programming principles this is an added advantage.

In a security platform that I worked on, I was testing for various scenarios - functional, unit, integration, stress, load, etc. However, a customer found bug taught me about regression testing that uncovered a pattern of edge cases that arise when new code changes break an existing functionality, thus loosing customer trust and experience. I came up with a regression test framework that is built as a separate pipeline with few assertions that checks the state of the system by inspecting the APIs. I configured the development pipeline to run the regression tests for 25% of the features in the first phase. Results were promising where the tests caught bugs from 2/27 code changes that could have broken the customer experience.

From an ML perspective, since input data you predict on can differ from input train data, the assumption you had may not be met and model training will fail. This is commonly seen more with statistical model, especially in the domain of time series forecasting. As such having testing edge cases, understanding how you want to fail gracefully and fallback are key to ensuring a good customer experience.

I have found automating the boundary value analysis and equivalence partitioning extremely helpful. Specifically for organizations that follow agile methodology for software development, there is always a balance between shipping and testing your releases/features. Any amount of testing is not sufficient. If you capture patterns of some of the edge cases pertaining to your application (i.e. I have often encountered spaces in empty fields), the best way to operate is to automate the functional testing and continue working on regression testing. That way, while you ensure that the application as a whole is perfect, the automation ensures that your current feature is flawless.

Indeed, Boundary Value Analysis is always beneficial. It's a good practice to explore equivalence classes, especially when dealing with scenarios like sorting arrays or lists. These equivalence classes may include an empty list, a reversed list, one with duplicate values, and mixed data types, among others.

One has to identify normal use cases before extreme conditions can be defined. I’d say the basic approach is to be as formal as possible with requirements definition. Formal requirements might not be easy to describe for a general system, but for an algorithm, it should just be part of the API definition. In conclusion, do the basic steps well and the corner cases will be much easier to find.

Depending on the complexity of the algorithm, something like Monte-Carlo simulation can help one cover a wide range of inputs. This type of testing allows you to cover uncommon combinations of inputs, as well as identify unexpected outputs.

Identifying edge cases and exceptions is akin to exploring uncharted territory in algorithm testing. Boundary value analysis is the compass here, focusing on values at or near input boundaries. It's about pushing the limits and testing extremes, uncovering vulnerabilities that might lurk there. Equivalence partitioning, on the other hand, segments the input domain into behaviorally similar groups. It's like categorizing the wild terrain you're exploring. Both methods are invaluable. Boundary analysis challenges the algorithm's resilience, probing for weaknesses, while equivalence partitioning allows us to examine different behavioral facets. Together, they provide a comprehensive view of how the algorithm responds under various conditions.

Designing test is not always easy. In general the recommendation should be, to clearly understand the behavior wanted and not-wanted - this is not necessarily easy. A recommendation is to test on every "layer" first, meaning having a focus on the specific structure of the code first, then test on interactions with the code and then testing on the behaviors by using the tools which is used already fro interacting with the product. A good step is also to also have a way of testing on a minimum of users, before releasing it. Making it possible to stop unwanted behavior quickly and create tests which encapsulate those behavior, so they hopefully do not arise again.

Start with well-defined test cases, specifying the exact conditions and inputs to be tested. When designing tests, it's crucial to consider the algorithm's purpose, complexity, and logic. Tailor your tests to align with your algorithm's specific requirements and constraints. Think about the various scenarios it might encounter and how it should handle them. This includes examining potential errors or pitfalls and understanding how exceptions are managed. Moreover, your tests should encompass not only accuracy but also efficiency and usability. This multifaceted evaluation ensures that your algorithm not only works correctly but does so in a way that meets performance expectations and provides a user-friendly experience.

Make sure you break down your algorithm into it's individual components. It's important to ensure each step in the algorithm is verified on its own, as well as how the steps interact with one another. Furthermore, typically an algorithm (and each algorithmic piece) has some window of operation. Detecting when inputs go outside this window, as well as how the algorithm transforms it's inputs into outputs (transfer function) allows you to identify where edge cases may occur.

To test edge cases Boundary Value analysis testing is one way to get all the test cases and also robustness testing which is a superset of BVA testing but the number of test cases generated will be huge depending upon the parameters in the function of an Algorithm or a system. The best way will be to automate the test cases for BVA such that the time consumed to generate test cases will be less.

At first, select a testing framework or tool tailored to your programming language and environment. This choice forms the foundation of your testing process, providing the means to execute and validate your tests effectively. Consider the scope-whether it's unit testing for individual components or broader integration and system testing. Use best practices and consistent naming conventions for test cases and data. Employ assertions or checks to validate expected outcomes. Measure the efficacy of your testing with code coverage or analysis tools. Lastly, be prepared to troubleshoot. Leverage debugging tools and logging mechanisms to pinpoint and resolve errors or exceptions as they arise.

Edge cases are the weird boundary conditions that aren’t explicit in the logic of your application, or aren’t clearly defined by your algorithm description that need to be handled for your application to remain in a consistent state. Writing "functional style" code generally results in fewer edge cases.

TDD is good approach for applications with intricate business edge cases, as it covers both the behavioral aspects and technical requirements of algorithms. It aids in detecting complex bugs in the early stages of development

From an ML modelling perspective, the most common exception occur due to data drift i.e. the data changes over time. Since testing for all such changes in data can be difficult, the best alternative is to use a reactive approach. Monitor model performance and output, and when you see it deviate from set standard, alert a developer and sample a certain amount of input data. The developer can improve the model and test their improved model on the data. Such sampled data over time, lets you build a more comprehensive set of scenarios to test for.

Improving tests for edge cases and exceptions requires a blend of vigilance, collaboration, and automation. Regularly review and refactor your test cases and data. Over time, your codebase may evolve, rendering some tests obsolete or incomplete. By revisiting and optimizing your tests, you ensure they remain aligned with your algorithm's functionality. Seek external perspectives. Developers, testers, or end-users can provide valuable feedback and fresh insights. Consider employing test automation or scripting tools to streamline the testing process. Automation reduces manual effort, enhances efficiency, and enforces consistency across tests. Adopt a test-driven or behavior-driven development approach.

It's essential to emphasize the proactive nature of testing edge cases and exceptions in algorithms. Rather than viewing it as a mere quality assurance step, consider it a strategic investment. Robust testing can uncover hidden vulnerabilities, enhance algorithmic performance, and ultimately lead to a more reliable and resilient software system. It's a testament to the commitment to quality and user satisfaction that every developer should embrace. The more thorough and imaginative you are in testing, the better equipped your algorithm will be to handle the unexpected challenges of real-world scenarios.

Let's say that we have software predicting patient drug interactions. Developers initially considered only prescription medications. However, after engaging with healthcare professionals, they learned about the significance of over-the-counter supplements. This stakeholder insight highlighted an edge case where a patient's supplement interacts adversely with a prescribed drug. Addressing this edge case in the algorithm could be vital in preventing potential health complications or even life-threatening situations.