In “TDD Guided by Zombies”, James Grenning shares a seasonally-appropriate acronym for developing unit tests — just in time for our class to wrap up its unit testing section!  ZOMBIES stands for Zero, One, Many/More, Boundary behaviors, Interface definition, Exercise exceptional behavior, Simple scenarios/Simple solutions.  The first three letters (Zero, One, Many) describe, in order, the complexity of the behavior undergoing testing — for example, a queue with zero, one, and then many entries.  The second set of letters (Boundary, Interface, Exercise Exceptions) give the order in which to build test cases — start with boundaries, design the interface undergoing testing (or ensure that the code meets the interface requirements), and then test exceptional behavior.  The last letter tells the tester to create simple scenarios with simple solutions — add the minimal possible behaviors to production code in order to pass the tests generated by the first six parts of the acronym.

Grenning spends the bulk of the article on an example of a circular queue implementation in C++, showing how he progresses through each step.  He gives clear examples of both what to do and not to do.  However, I’m more concerned with the underlying principles and process, so in the interest of brevity I’ll skip a detailed review of his example and simply say that it’s thorough and worth a longer period of study.

I chose this article not just because of the season (or that it coincides neatly with our in-class work), but because it helps to answer the question of how.  We’ve learned many methods for generating unit test cases, but how do we pick the order?  How do we work backwards from tests to code in a way that makes sense and satisfies the specification that we’re handed?  This confirms what I learned in 343 last year: ZOMBIES are the key to TDD and to successful unit testing.

And what can I, as a learner, take away from this?  Firstly, and perhaps most trivially, it can help to wrap important concepts in cute acronyms.  It makes sharing and remembering knowledge easier.  Secondly, it clarifies the most important sets of values to test, and the most important times at which to test newly-instantiated objects (or other language-apprpriate constructs): when they are fresh and empty, when they have a single value, and when they contain many values.  Thirdly, the article really drives home the importance of minimalism in testing and test-driven coding.  If a simple solution is all that it takes to meet the specification, then use it.  If a complex solution can be simplified, simplify it.

This semester and onward, when I need to develop unit test cases, I’ll be thinking ZOMBIES.

Article link.

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

Flaky tests are tests that could pass or fail for the same code. This is a problem because the failure of a flaky test doesn’t always indicate a problem with the code, but you can’t just ignore the test because you could be ignoring a bug.

In the the blog post “Where do our flaky tests come from?”, Jeff Listfield, a Senior Software Engineer at Google, talks about the the potential causes of flaky tests and what can be done to avoid creating flaky tests. He demonstrates the correlation between the objective size of the test (binary size, memory usage) and the likelihood for it to be flaky. He also shows a correlation between certain tools and a higher rate of flaky tests, however, the reason for this is because larger tests are more commonly written using those tools. The tools themselves only contribute a small amount to the likelihood of a flaky test being created. When writing tests you should think about what code that you are testing and what a minimal test would look like in order to minimize the likelihood of creating a flaky test.

I chose this topic because effective use of test cases is very important in software development because it allows you to make sure that you’ve addressed and tested all of the product requirements, allows future testers to run your test cases when needed, and also makes it possible to build automated scripts to run as many tests as possible. By writing your test cases out, you also won’t need to constantly repeat the process and remember what values you’re testing every time as they’ll already contain all necessary variables, allowing you to maintain consistency in your tests.

This blog post in particular was interesting because it uses data gathered from real tests used in Google’s continuous integration system to show a cause of flaky tests and how to avoid them. Before reading this blog I didn’t realize how important writing test cases really was, but seeing just how many automated tests that Google used (4.2 million!) which lead to me doing more research on their importance. It also reminded me that the best solution is usually the simplest, in that you should remember to keep your test cases as simple as possible to avoid creating a flaky test and giving you a headache in the future trying to figure out what’s wrong.

Source: https://testing.googleblog.com/2017/04/where-do-our-flaky-tests-come-from.html

From the blog CS@Worcester – Andy Pham by apham1 and used with permission of the author. All other rights reserved by the author.

Flaky tests are tests that could pass or fail for the same code. This is a problem because the failure of a flaky test doesn’t always indicate a problem with the code, but you can’t just ignore the test because you could be ignoring a bug.

In the the blog post “Where do our flaky tests come from?”, Jeff Listfield, a Senior Software Engineer at Google, talks about the the potential causes of flaky tests and what can be done to avoid creating flaky tests. He demonstrates the correlation between the objective size of the test (binary size, memory usage) and the likelihood for it to be flaky. He also shows a correlation between certain tools and a higher rate of flaky tests, however, the reason for this is because larger tests are more commonly written using those tools. The tools themselves only contribute a small amount to the likelihood of a flaky test being created. When writing tests you should think about what code that you are testing and what a minimal test would look like in order to minimize the likelihood of creating a flaky test.

I chose this topic because effective use of test cases is very important in software development because it allows you to make sure that you’ve addressed and tested all of the product requirements, allows future testers to run your test cases when needed, and also makes it possible to build automated scripts to run as many tests as possible. By writing your test cases out, you also won’t need to constantly repeat the process and remember what values you’re testing every time as they’ll already contain all necessary variables, allowing you to maintain consistency in your tests.

This blog post in particular was interesting because it uses data gathered from real tests used in Google’s continuous integration system to show a cause of flaky tests and how to avoid them. Before reading this blog I didn’t realize how important writing test cases really was, but seeing just how many automated tests that Google used (4.2 million!) which lead to me doing more research on their importance. It also reminded me that the best solution is usually the simplest, in that you should remember to keep your test cases as simple as possible to avoid creating a flaky test and giving you a headache in the future trying to figure out what’s wrong.

Source: https://testing.googleblog.com/2017/04/where-do-our-flaky-tests-come-from.html

From the blog CS@Worcester – Andy Pham by apham1 and used with permission of the author. All other rights reserved by the author.

Flaky tests are tests that could pass or fail for the same code. This is a problem because the failure of a flaky test doesn’t always indicate a problem with the code, but you can’t just ignore the test because you could be ignoring a bug.

In the the blog post “Where do our flaky tests come from?”, Jeff Listfield, a Senior Software Engineer at Google, talks about the the potential causes of flaky tests and what can be done to avoid creating flaky tests. He demonstrates the correlation between the objective size of the test (binary size, memory usage) and the likelihood for it to be flaky. He also shows a correlation between certain tools and a higher rate of flaky tests, however, the reason for this is because larger tests are more commonly written using those tools. The tools themselves only contribute a small amount to the likelihood of a flaky test being created. When writing tests you should think about what code that you are testing and what a minimal test would look like in order to minimize the likelihood of creating a flaky test.

I chose this topic because effective use of test cases is very important in software development because it allows you to make sure that you’ve addressed and tested all of the product requirements, allows future testers to run your test cases when needed, and also makes it possible to build automated scripts to run as many tests as possible. By writing your test cases out, you also won’t need to constantly repeat the process and remember what values you’re testing every time as they’ll already contain all necessary variables, allowing you to maintain consistency in your tests.

This blog post in particular was interesting because it uses data gathered from real tests used in Google’s continuous integration system to show a cause of flaky tests and how to avoid them. Before reading this blog I didn’t realize how important writing test cases really was, but seeing just how many automated tests that Google used (4.2 million!) which lead to me doing more research on their importance. It also reminded me that the best solution is usually the simplest, in that you should remember to keep your test cases as simple as possible to avoid creating a flaky test and giving you a headache in the future trying to figure out what’s wrong.

Source: https://testing.googleblog.com/2017/04/where-do-our-flaky-tests-come-from.html

From the blog CS@Worcester – Andy Pham by apham1 and used with permission of the author. All other rights reserved by the author.

soft qual ass & test

From the blog CS@Worcester – BenLag's Blog by benlagblog and used with permission of the author. All other rights reserved by the author.

Second blog

From the blog CS@Worcester – Not just another CS blog by osworup007 and used with permission of the author. All other rights reserved by the author.

Second blog

From the blog CS@Worcester – Not just another CS blog by osworup007 and used with permission of the author. All other rights reserved by the author.

This week i picked software frameworks. Since it’s going to be a next to topic to be discoursed in class in the future. I rather prepare my self, and get more understanding.

A software framework is a concrete or conceptual platform where common code with generic functionality can be selectively specialized or overridden by developers or users. Frameworks take the form of libraries, where a well-defined application program interface (API) is reusable anywhere within the software under development

Here are some types of software frameworks:

• Resource Description Framework, a set of rules from the World Wide Web Consortium for how to describe any Internet resource such as a Web site and its content.
• Internet Business Framework, a group of programs that form the technological basis for the mySAP product from SAP, the German company that markets an enterprise resource management line of products
• Sender Policy Framework, a defined approach and programming for making e-mail more secure
• Zachman framework, a logical structure intended to provide a comprehensive representation of an information technology enterprise that is independent of the tools and methods used in any particular IT business

Using a framework is not really any different from classic OOP programming.

When you write projects in a similar environment, you will probably see yourself writing a framework (or a set of tools) over and over again.

A framework is really just code reuse – instead of you writing the logic for managing a common task, someone else (or you) has written it already for you to use in your project.

A well designed framework will keep you focused on your task, rather than spending time solving problems that has been solved already.

Frameworks of all kinds are extremely important nowadays, because of the time factor. When building something you will need to invest a lot of your time in building the logic for your application – and you don’t want to be forced to program any kind of low-level functionality. Software frameworks do that, they take care of the low-level stuff for you.

There are some this disadvantages :

• Creating a framework is difficult and time-consuming (i.e. expensive).
• The learning curve for a new framework can be steep.
• Over time, a framework can become increasingly complex.

But these disavantages , i think its the best way to go.

From this topic i learned a framework is a code reuse, extremely important for programmer for it to take care of the low-level stuff. This will also help me develop better codes and in a fast pace .  I really hope this help all the students taking this cs-343 class for better understanding in the future.

links or reference :: https://www.techopedia.com/definition/14384/software-framework ,

http://whatis.techtarget.com/definition/framework

From the blog CS@worcester – Site Title by Derek Odame and used with permission of the author. All other rights reserved by the author.