I chose the article “Software Analysis and Design Tools” because I think it’s important to know how to put design knowledge to work using the common tools and practices. Software design and analysis allows the requirements of an application to be converted to actual code. I chose Tutorials Point for this article because I’ve used them quite a bit as I’ve learned different topics in computer science.
The first tool that is mentioned is a data flow diagram. This is basically a graphical representation of the path of data in an information system including incoming, outcoming, and storage. It’s important to know that this flow does not convey underlying “how” the data actually flows, it just shows the path. The two types of DFD’s are logical and physical and they use a set of components to represent data flow and relationships. This includes entities, processes, data storage, and data flow which are organized into different levels which represent a layer of abstraction.
The next tool used is called a structure chart which is actually derived from a data flow diagram. It shows a system in much more detail down to the lowest functional modules and describes the functions of these modules. The actual chart depicts a hierarchy of modules where each layer performs a specific task. The charts use special symbols to represent things like conditions, jumps, loops, data flow, and control flow.
The next tool is called a HIPO diagram which stands for Hierarchical Input Process Output. This diagram represents a hierarchy or modules in a system and depicts all the functions and sub-functions of a module. They are a good tool to represent system structure and allow designers and managers to picture the overview of a system.
There is also a diagram called IPO which stands for Input Process Output. This diagram shows a good representation of control and data flow in a module as the HIPO diagram does not depict the flow of any data.
Some of the other tools mentioned in the article include pseudo code, decision tables, entity-relationship models, and data dictionaries.
After reading this article I think I realized there’s a step in the software design implementation that I’ve been overlooking. Creating these models and diagrams mentioned is a pre-cursor to choosing a design pattern or implementing any code. Creating a usable understanding of how a system will work is a crucial first step in any design. I think this article did a really good job or covering some of the more popular software analysis and design tools. When it comes time to design an application I will definitely make sure I’ve represented the system using one of these tools before I think about the actual design pattern or architecture I want to use.
I chose this topic because I tend to forget exactly what kinds of tests are being done when system testing is mentioned. The term is very vague so I thought it’d be smart to write my own explanation of it. I chose the specific article “What is System Testing?” because I’ve used articles from this website before and have found them to be helpful.
To start, system testing is the testing of a complete and integrated software system. The important part of this is to recognize an entire system and not just one program. Interfaces, programs, and hardware are integrated frequently and there needs to be a way to test how they all work as a system. System testing is how this can be achieved.
System testing is one type of black box testing (as opposed to white box testing). System tests examine how software works based on a user’s perspective. One of the things being tested are the fully integrated applications in an end to end testing scenario. This checks for how components interact with each other as well as the system as a whole. Inputs in the application will be tested and compared to what is the expected outputs are. Lastly, a user’s experience is also part of the system testing to ensure the system flows and can be navigated smoothly by the end user.
While system testing is extremely important it’s not the only type of testing needed and it’s important to realize when it needs to be conducted. Typically it will be towards the end of the development cycle as most of the system needs to be operating correctly in order to properly execute the test cases. Unit testing and integration testing will come prior to system testing and usually acceptance testing will follow.
While there are quite a few types of system some of the more popular types include usability, load, regression, recovery, and migration.
After reading this article I definitely have a better and clear understanding of what system testing is. I think this article gave good explanations and included a helpful video as well. In order to actually implement system testing on a project I’d need to do some further reading on one of the specific types in order to decide what type best covers my system. I haven’t had the need to really implement system testing so far mainly because my programs have been relatively simple. As I head to the work force this type is testing will become very important working on much larger systems.
I chose the topic after googling about the different types of software architecture and realizing I’d never read about microservices. I chose this specific article titled “Microservices” because I know Martin Fowler’s blog contains reputable information as he’s also been referenced in class. Microservices architecture is a way of designing applications as groups or suites that can be deployed independently as a service. While he mentions there is no exact definition, this style usually has similar characteristics around capability, automated deployment, endpoints intelligence, and decentralized control of data.
The microservice style is a newer and more common approach for enterprise applications. It’s a single application as a suite of small services. Each service runs in its own process and usually communicates with an HTTP resource API.
One key feature of microservices architecture is componentization via services. The different services of an application are a way to break down the software by components. These services are out of process components that communicate using web service requests. An advantage of using services as components instead of libraries is that they can be deployed independently. Therefore only requiring a single service to be deployed when there is a change.
A second key feature of microservices architecture is that it is organized around business capabilities. This approach combats the negative effects of separate teams working on an application as management usually splits focus between the technology layer, leading UI teams, server-side logic teams, and database teams. Services allow teams to be cross functional and include a full range of skills as products can be split up by individual services and communicate via a message bus.
Some of the more brief features of microservices architecture include the idea that a team should own a product over it’s lifetime and not just treat it as a project. Additionally microservices usually follow a decentralized governance which is less constricting and allow each service to take advantage of different technology that best suits the service. Lastly decentralized data management is common using microservices. Typically each service will manage its own database.
After reading this article I definitely have an idea of what microservices architecture is but I think I’d need a more beginner level article to explain it. There were definitely some terms and concepts that Martin referred to that I wasn’t familiar with. One thing I did like about the Article was that he mentioned how well known companies use some of the technology he was talking about such as Amazon and Netflix. Seeing as microservices are mainly used for enterprise applications I have yet to gain any experience with them but most likely will in the near future.
For this blog I’ll be covering the basics of functional testing based on the article I read titled “Functional Testing Tutorial“. Although this topic is a pretty broad topic, it will compliment my previous blog covering “What is non-functional testing?” Functional testing is when each function of an application is tested and verified that it satisfies the specifications and requirements. Most functional testing is black box testing and does not deal with any of the actual code. To test all of the functions in an application testers provide input to the function and verify the output with what is expected. This is carried out either by manual effort or automated testing.
Aside from testing each individual function, functional testing also checks system usability, system accessibility, and error conditions. Basically a user should not have difficulty using a system and in an error condition, the correct error message or procedure should be followed. In order to carry out functional testing there is a basic testing process that must be followed. First identify the test data or input, then calculate the expected outcome and values, execute your test cases, and last conduct a comparative analysis to make sure all expected outputs match the actual outputs.
The article moves on to compare functional and non-functional testing to give an idea when each is used. Functional testing is usually done first and can be manual or automated. The testing coverage is used to ensure business requirements are met based on inputs. Functional testing is more a description on what the product or system actually does. There are a lot of ways to implement functional testing for a system. Some of the most popular types are Unit Testing, Smoke Testing, Sanity Testing, Integration Testing, White Box Testing, Black Box Testing, User Acceptance Testing, and Regression Testing. A few of the popular tools used to execute these tests include Selenium, Junit, and QTP.
After reading this article I’m confident I’ll be able to classify a type of testing between functional and non-functional. The article didn’t go too specific into the execution of functional testing but that’s because there are just too many types to generalize the implementation. When I want to actually execute and implement functional testing I’ll have to read more in depth into one specific type in order to actually test a system. I think the most important concept of functional testing is to remember that the business requirements are most important. That being said it will be important to make sure the business requirements are fully understood through the development cycle to ensure proper test coverage.
For this week’s blog I chose the article titled “Alpha Testing vs Beta Testing.” I chose this article because it covers two types of testing I haven’t read too much about. I also like the comparison type so I can see different situations why I might choose one over the other.
To start, alpha testing is a type of acceptance testing. It’s used to identify all the possible issues in a product before it gets released to users. The idea of this type of testing is to simulate real users by using blackbox and whitebox methods. The article mentions this type of testing is usually done by internal employees in a lab type environment. The overall goal is perform tasks that the typical user will be doing frequently. This testing is done near the end of the software development cycle but before beta testing if beta testing is being done.
Beta Testing is another form of acceptance testing done by real users in a real environment. It’s mainly used to gather feedback and limit product risks before the product gets released to anyone and not just a small testing group. This would be the last type of testing before a final product gets shipped to customers.
While beta testing and alpha testing share some similarities there are some key differences. The first being that in beta testing reliability, security, and robustness are checked which is not true for alpha testing. Another difference is how issues are addressed. For alpha testing it’s not uncommon to make code changes before an official release. With beta testing code changes will usually be planned for future versions after the product is released. Lastly, with beta testing you are getting feedback from real users and this will usually be a more accurate analysis of how a product will perform over alpha testing.
For larger product firms, a product release will usually incorporate both alpha and beta testing. Below is a typical flow chart of the process.
To clarify, the pre alpha phase would be a prototype where not all features have been completed and the software has not been officially published. The release candidate phase is when any bug fixes are small feedback based changes have been made.
In conclusion, this article was really great comparing alpha and beta testing. It goes into more details with some advantages and disadvantages of the two as well as some entry and exit criteria however this goes beyond the scope of this blog. After reading about these two types of testing I would definitely want to include both in a product release strategy however I would choose beta testing if I could only choose one. I think real user feedback in a real time and natural environment is most valuable before releasing. At the same time it would be easy to argue that terrible feedback in a beta testing cycle could be prevented with prior alpha testing.
For this week’s blog I chose an article on the pipe and filter architecture appropriately titled “Pipe-And-Filter.” I chose this article after googling what some of the most common software architectures are and learning that pipe and filter was commonly implemented. This article seemed like a good length with straightforward information and diagrams to help with understanding the material so that is what I chose it.
To begin, this architecture consists of any number of components referred to as filters due to the fact that they filter data before passing it through connectors called pipes to other components. All of the filters work at the same time and this is usually implemented in simpler sequences although is not limited to that.
Above is a simple diagram to show how the architecture flows. It’s important to know that filters can transform the input data from any number of pipes. The pipes pass data between filters however it is unidirectional implemented by a buffer until the downstream filter can process it. The pump is where the data originates such as a text file or I/O device. Lastly the sink is the end target of the transformed data such as a file, database, or output to a screen.
One good example of this architecture would be a Unix program. One program’s output can piped into another program’s input.
Above is a more complex diagram to show how pipe and filter can start to become complex. Different sources or pumps can interconnect data into their respective streams. An application that uses this architecture will typically link all the components together and then spawn a thread for each filter to run in.
One interesting functionality of this pattern is a recursive filter technique. This is implemented by having a filter inside of another filter.
One common issue with this type of architecture concerns what kind of data types are allowed in a certain pipe. If only one type is allowed, filters need to parse for this which can slow an application down. You may also limit yourself to what pipes can connect to which filters.
After reading this article I have a good idea of pipe and filters main concepts. One thing I wished the article had discussed more in detail would be specific implementations of this architecture. I can’t directly see how I would need to use these concepts in any of the coding I’ve done so far. I can see a general use for this model for an application that takes in a lot of raw data and needs to output it in a useful format for making business decisions. In summary this was a well written article but I need to do some further reading on implementation examples.