Raw Coverage vs. Meaningful Tests If you’re shooting for a high-coverage number at the expense of a solid, meaningful, clean test suite, you lose value. A well-maintained suite of tests gives you confidence in your code and is even the basis for quickly and safely refactoring. Noisy and/or meaningless tests mean that you can’t rely … Continue reading Test Coverage →

From the blog cs-wsu – Kristi Pina's Blog by kpina23 and used with permission of the author. All other rights reserved by the author.

In this post I will be looking at mutation testing. This is aimed at people who are not familiar with mutation testing. I will give an introduction to what mutation testing is; show some examples of what it can do; consider its limitations and look at how mutation testing can be added to a Java … Continue reading Mutation – Testing your tests →

From the blog cs-wsu – Kristi Pina's Blog by kpina23 and used with permission of the author. All other rights reserved by the author.

When Should You Use React? React shines when you have lots of dynamic content changing within the view. Most client-side solutions on the web today struggle with rendering large lists of items within a single view. This “struggle” may be on the order of milliseconds, but in this increasingly digital world, a half a second … Continue reading Angular vs. React →

From the blog cs-wsu – Kristi Pina's Blog by kpina23 and used with permission of the author. All other rights reserved by the author.

I am writing in response to the blog post at https://blog.codinghorror.com/your-code-oop-or-poo/ titled “Your Code: OOP or POO?”

Most of the code I have ever written has been founded in the imperative programming paradigm. I began pulling concepts from object oriented programming in the last few years to help organize large projects and keep the structure of things more adaptable. It is a useful way to develop a large framework, but relying on objects for absolutely everything seems like it would tend to cause a lot more problems than it would ever be meant to solve.

This blog post refers to “POO” as “Programming fOr Others”, as opposed to plain Object Oriented Programming (OOP). One of the purposes of object oriented programming, and one of the reasons it is such a dogmatically adhered-to convention, is that it makes it a lot easier for people who did not write the code to understand what it is doing and work with it themselves. The blog post discusses on the topic that object oriented programming can be over-used, and it is not the only solution to writing code that is readable and easy to be understood by others.

There was an excerpt about how there was a type of programmer who would only write five or ten lines of code, preceded by twenty lines of comments, and object oriented programming basically allows the two to be combined. Instead of concise code accompanied by an explanation, there is a large file filled with descriptive language embedded into the structure of the code itself.

Another excerpt mentioned the use of object oriented programming for trivial tasks. I do not think that it makes any sense to go through the effort of supporting scalability and maintainability for something that can be started and completed and discarded so easily without the extra work. A simple command line in an imperative programming language would be far more practical for basic tasks.

Part of the argument made is that a programmer should focus on the principles of object oriented programming, rather than the name. Encapsulation, simplicity, code re-use and maintainability are the main ideas, not just objects.

From the blog CS@Worcester – klapointe blog by klapointe2 and used with permission of the author. All other rights reserved by the author.

I am writing in response to the blog post at https://www.guru99.com/grey-box-testing.html titled “Grey Box Testing: Process, Techniques, Strategy, Challenges”.

Grey box testing, as the name suggests, is meant to be a combination of black box testing and white box testing. In white box testing, the internal structure of the code is known, and in black box testing, the structure of the code is unknown, and in grey box testing, it is partially known. Grey box testing combines the benefits of black box testing and white box testing, combining the input of developers while testing from the perspective of a user.

An example of grey box testing is given where a tester is testing a website and something is wrong with the HTML code, so the tester is able to look at and make changes to the code themselves to continue testing. A few other types of testing are listed as qualifying as being in the category of grey box testing: matrix testing, regression testing, orthogonal array testing, and pattern testing. These methods follow the strategy of black box testing while integrating some white box testing practices to focus more on what the test cases should be looking for.

A sequence of steps is provided for how to perform grey box testing. The first two steps, identifying inputs and outputs, do not seem to be directly related to black box testing or white box testing alone. In black box testing, there would be no need to explicitly identify the inputs and outputs; they would already be given in the test specification. The creation of the specification does require access to the code, which relates to white box testing, so grey box testing in this sense seems like a real-time testing approach where there is not necessarily a pre-developed specification and learning about the environment is a part of the testing process.

Challenges in grey box testing that are listed include when a test executes and the result is incorrect, and when something being tested encounters some failure. These do not really sound like challenges, they seem to just be examples of what testing is for.

From the blog cs-wsu – klapointe blog by klapointe2 and used with permission of the author. All other rights reserved by the author.

I am writing in response to the blog post at https://9clouds.com/blog/premature-optimization-the-root-of-all-evil/ titled “Premature Optimization: The Root of All Evil”.

Premature optimization is about focusing on making sure the code will be able to run as fast as possible before anything actually works yet. It is time consuming and, by definition, “premature”, so it is not a good thing to do. The blog post quotes Donald Knuth who said “Premature optimization is the root of all evil.” For sizable projects, premature optimization is practically procrastination. It is about making sure that the program works well in theory before making sure that it works at all. The blost post seems to be referring to premature optimization on the scale of businesses, where I am only considering the effect on individual programs and projects, but the effects are the same. It makes more sense to finish something that works and then improve it than it does to improve it first and then finish it.

I have done a lot of premature optimization on small projects, and it certainly does take up a lot of time. I tend to do it just because it is interesting, though, to try to come up with such an implementation that performs optimally. It is a secondary challenge that puts off the original task. If it becomes tedious or starts to seem wasteful then I just write something that works and move on to the next part, and if it matters or makes a difference, and it never does, I can just look into how to make things faster again. The idea of actually producing something in a professional sense and becoming caught up in the performance details on a small scale before the rest of the product is finished definitely seems like it would be a waste of time. Premature optimization requires making predictions of how things are going to work after they are made, and devising a plan to make things fit together smoothly before the things even exist yet. It makes much more sense to make the things first, and then make them work together better afterwards.

From the blog cs-wsu – klapointe blog by klapointe2 and used with permission of the author. All other rights reserved by the author.

I am writing in response to the blog post at https://www.guru99.com/back-box-vs-white-box-testing.html titled “Black Box Testing Vs. White Box Testing: Key Differences”.

Black box testing and white box testing are topics that we have covered in the CS 443 software quality assurance and testing class. Black box testing involves testing from the perspective of a user, who does not have access to the code, and thus the testing is done without referring to the code. Instead, the behavior is tested directly by checking inputs against expected outputs. White box testing involves code coverage and testing different branches and paths of code based on the code itself and not a pre-defined specification for the behavior to meet.

The blog post provides a table of comparisons between black box testing and white box testing. Most of the points are that black box testing is done without access to the code and white box testing focuses on the code. One of the points made is that black box testing is not good for testing algorithms. This makes a lot of sense, and for certain algorithms, black box testing would be impossible to do. Black box testing requires some specification for the behavior of the program to meet, and that specification is supposed to cover everything that might go wrong with the program. This is fine when the program is made up of a set of conditional branches, but something more intricate like a computational geometry or machine learning algorithm would be impossible to test every case for because the number of different paths the code can take is on a completely different scale. White box testing in this case would be the only way to check that the algorithm will work, and that is by proving that it works. The only way black box testing could prove correct behavior would be with an exhaustive proof.

White box testing is not necessarily as easy or convenient as black box testing because it requires understanding and analyzing the code instead of just running it and seeing what happens. In some cases, though, it is necessary to use.

From the blog CS@Worcester – klapointe blog by klapointe2 and used with permission of the author. All other rights reserved by the author.

What is Security testing? Security Testing is a variant of Software Testing which ensures, that system and applications in an organization, are free from any loopholes that may cause a big loss. Security testing of any system is about finding all possible loopholes and weaknesses of the system which might result into a loss of … Continue reading Security Testing →

From the blog cs-wsu – Kristi Pina's Blog by kpina23 and used with permission of the author. All other rights reserved by the author.

Static Testing also is known as ‘dry run testing’ is a software testing technique used during SDLC (Software Development Life Cycle) that involves examination of the code prior to the program’s execution. It does not actually require the application to be executed. Static Testing may be of specific types and include code analysis, inspection, code reviews, data … Continue reading Static Analysis →

From the blog cs-wsu – Kristi Pina's Blog by kpina23 and used with permission of the author. All other rights reserved by the author.

I am writing in response to the blog post at https://www.guru99.com/difference-information-data.html titled “Difference between Information and Data”. This is a topic that was covered in my Database Design and Applications class, but it is still a useful distinction to be familiar with for the purposes of software testing.

The blog post firstly individually defines data and information. Data is defined as a raw, unorganized collection of numbers, symbols, images, etc. that is to be processed and has no inherent meaning in and of itself. Information, on the other hand, is defined as data that has been processed, organized, and given meaning. My general understanding of the difference between information and data is that data is information without context, and that seems to be consistent with what how the blog post is explaining it.

There is a long table of contrasting descriptions of different attributes of data and information listed on the blog post; data has no specific purpose, it is in a raw format, it is not directly useful and has no significance, whereas information is purposeful, dependent on data, organized and significant. One particular property is labeled “knowledge level”, where data is described as “low level knowledge” and information is said to be “the second level of knowledge.” I have never considered “levels” of knowledge before, this seems to suggest that there are multiple additional categories. It later mentions “knowledge” and “wisdom” as additional categories. DIKW (Data Information Knowledge Wisdom) is explained, which is something I have never heard of before. It is a model used for discussing these categories and the relationships between them. An example it provides lists an example value for data as “100”, information as “100 miles”, knowledge as “100 miles is a far distance”, and wisdom as “It is difficult to walk 100 miles by any person, but vehicle transport is okay.” These additional levels of knowledge seem to further process and contextualize the information. I think that it would have been interesting if it expanded further on these topics and how things like knowledge and wisdom are significant independently from data and information, and whether further levels of knowledge exist even beyond that.

From the blog CS@Worcester – klapointe blog by klapointe2 and used with permission of the author. All other rights reserved by the author.