As I was reading about coupling and cohesion, I also came across the Principle of Least Knowledge, which just so happened to be on our list of topics in the syllabus.

The Principle of Least Knowledge (or Law of Demeter) was first discussed in 1987 at Northeastern University. It states that an object should never now the internal details of other objects, which makes sense given the name. It is used to promote loose coupling in software designs.

The article also gives an example using 3 classes – A, B, and C. Each class has objects objA, objB, and objC. objA is dependent on objB, which in turn composes objC. In this scenerio, objA can invoke methods and properties of objB but not objC. Say C has a method M. If you make a objC called O, the Principle of Least Knowledge says O can access

• The same object, i.e., the object “O” itself
• Objects that have been passed as an argument to the method “M”
• Local objects, i.e., objects that have been created inside the method “M”
• Global objects that are accessible by the object “O”
• Direct component objects of the object “O”

A code example is also given to illustrate the concept.

public class LawOfDemeterExample

    {

        //This is an instance in the class scope

        //and hence this instance can be accessed by any members of this class

        AnotherClass instance = new AnotherClass();

       public void SampleMethodFollowingLoD(Test obj)

        {         

            DoNothing(); //This is a valid call as you are calling a method of the same class

             object data = obj.GetData(); //This is also valid since you are calling a method

            //on an instance that has been passed as a parameter           

             int result = instance.GetResult();  //This is also a valid call as you are calling

            //a method on an instance locally created

        }

        private void DoNothing()

        {

            // Write some code here

        }

    }

After, an second example is given to illustrate what not to do.

var data = new A().GetObjectB().GetObjectC().GetData();\

In this example the client depends on all three classes A, B, and C. If any of the three change you will run into issues.

This principle will definitely be important as I continue my career in software development. As the ode I write gets more complex, I have to be wary of violating principles like this to maintain low coupling. The author also mentions testability being improved with this principle and the loosely coupled code it encourages. Next semester I am taking the software testing course; right now I have next to no knowledge about software testing. I only know how to just test units made for me in java. Hopefully I will see how loosely coupled code compared to tightly coupled code in testability.

From the blog CS@Worcester – Half-Cooked Coding by alexmle1999 and used with permission of the author. All other rights reserved by the author.

This week I wanted to learn more about coupling. The article I found happened to be about both coupling and cohesion.

Coupling and cohesion are both terms that refer to the modularity in a system. They help measure how complex the design of an object-oriented system is. Good knowledge of both is needed to build scalable systems that can be extended and maintained over time.

Coupling is how interdependent software modules are. With high coupling, software modules are very independent, and they need each other to run. There are several types of coupling:

• Content coupling — this is a type of coupling in which a module can access or modify the content of any other module. When a component passes parameters to control the activity of some other component, there is a control coupling amongst the two components.
• Common coupling — this is a type of coupling in which you have multiple modules having access to a shared global data
• Stamp coupling — this is a type of coupling in which data structure is used to pass information from one component in the system to another
• Control coupling — this is a type of coupling in which one module can change the flow of execution of another module
• Data coupling — in this type of coupling, two modules interact by exchanging or passing data as a parameter

Cohesion is how intra-dependent a software module is. High cohesion is a software module that forms its own meaningful unit. There are also different types of cohesion:

• Co-incidental cohesion — this is an unplanned random cohesion that might be a result of breaking a module into smaller modules.
• Logical cohesion — this is a type of cohesion in which multiple logically related functions or data elements are placed in the same component
• Temporal cohesion — this is a type of cohesion in which elements of a module are grouped in a manner in which they are processed at the same point of time. An example could be a component that is used to initialize a set of objects.
• Procedural cohesion — this is a type of cohesion in which the functions in a component are grouped in a way to enable them to be executed sequentially and make them procedurally cohesive
• Communicational cohesion — in this type of cohesion the elements of a module are logically grouped together in a way that they execute sequentially and they work on the same data
• Sequential cohesion — in this type of cohesion the elements of a module are grouped in such a manner that the output of one of them becomes the input of the next — they all execute sequentially. In essence, if the output of one part of a component is the input of another, we say that the component has sequential cohesion.
• Functional cohesion — this is the best and the most preferred type of cohesion in which the degree of cohesion is the highest. In this type of cohesion, the elements of a module are functionally grouped into a logical unit and they work together as a logical unit — this also promotes flexibility and reusability.

Tight coupling makes maintenance difficult as all the components depend on each other.

This article was helpful in explaining these two concepts. As we develop increasingly complicated software in our courses and careers it is important to learn more abstract concepts. These were things I have never really thought about before. Usually I write code just so that it works. If I am going a step further I might try to make it “look good” and be neatly indented with as few lines as possible. Especially going into the capstone course, I will have to evaluate my code for things like low coupling and high cohesion.

https://www.infoworld.com/article/2949579/design-for-change-coupling-and-cohesion-in-object-oriented-systems.html

From the blog CS@Worcester – Half-Cooked Coding by alexmle1999 and used with permission of the author. All other rights reserved by the author.

            In our practice with REST API backends, multiple times we ran into MongoDB usage, as the database we were using for the API was MongoDB. My only previous experience had been SQL/SQLite, so I wanted to know what the difference was. I came to find out there is a world of difference, in fact MongoDB is a NoSQL database (NoSQL meaning… well exactly what you would expect it to mean). I found an article – part of a MongoDB tutorial – from Guru99 called Types of NoSQL Databases, What is & Example which dove into the differences and usefulness of MongoDB and other NoSQL databases. Chiefly, NoSQL DB’s do not require fixed schemas, avoid joins, and are easy to scale, which is perfect for distributed data storage and “scaling out”, which is becoming increasingly essential in the CS world. The lack of schema is convenient too, as it allows for “heterogeneous structures of data in the same domain,” providing for great versatility. The other essential parts of NoSQL include it being non-relational and having a relatively simple API. The four kinds are Key-value pair based, column-oriented graph, graph based, and document oriented. Key-value pair based means data is stored in pairs (with a key and a value), which is optimal for large datasets and heavy loads. Column-based means that work is done on separated columns with values in single columns being stored contiguously, performing well with aggregation queries. Document-oriented DB’s (MongoDB is one) also function as a key/value pair, but in this, a value is stored as a document, which is stored in JSON or XML. It is not optimal for high performance or aggregate structures, like Key Value or Column Based. Graph based stores entities and relationships between them. Each entity is a node and each relationships is an edge, defining the relationship. This is different from relational DB’s inn that Graph DB’s are “multi-relational.” NoSQL DB’s commonly query with a key and a GET request. The CAP theorem is also important to NoSQL because it guarantees at most two out of the following three: consistency, availability, and partition tolerance. This is a key limitation of NoSQL DB’s. The last key to NoSQL falls on the concept of eventual consistency, meaning that changes to data on one machine must be reflected on other replicas eventually. Where the standard for RDBMS is ACID, NoSQL is BASE: Basically Available, Soft state, Eventual consistency. The article goes on the list advantages, disadvantages, and summarize itself before coming to a close. I thought this was very interesting to learn, as I can see how different DB styles can reflect the needs of a project or client story. I wasn’t sure why we were using MongoDB; I kind of took it as a given. But this reading broadened knowledge, so now, if I am able to build a REST API of my own over the upcoming break, perhaps I can find the optimal DB for my needs, or help me better understand future projects once I join the workforce.

From the blog CS@Worcester – Marcos Felipe's CS Blog by mfelipe98 and used with permission of the author. All other rights reserved by the author.

https://blog.codinghorror.com/code-smells/

This blog post will center on code smells which was a topic covered in class fairly early on. The reason why I’m going back to it is mainly to refamiliarize myself with the subject since it wasn’t a major topic in class. Code smells are defined by the blog post linked above as general warning signs in code, hence the name. There are different types of code smells that signify different coding problems. For example, long comments, long methods, overly large classes, duplicate code, dead code, inconsistent naming, uncommunicative naming, et cetera. Keep in mind that these issues won’t necessarily stop a program from running, rather they negatively effect on the performance and design levels. Also, all of these different types of code smells share the common trait of not quite looking right when looking at a larger scale. So at first glance the code might seem fine, but given a closer look the flaws whether it be dead code, inconsistent naming, or whatever become readily apparent.

A code smell isn’t something concrete like a detailed recipe, it’s more abstract than that. It similar to a suggestion or a tip and can lead to improving code both on a functional level and a design level. Take for example, noticing the code smells of long comments and dead code which suggest removing the useless code and either simplifying or outright deleting the comments. This will make the code more readable which also improves it on a design level. On the other hand, noticing the code smells of duplicate code and long methods and correcting each issue accordingly will improve the code on a performance level top of the possible design improvements. All of the types of code smells that I listed earlier still hold true but they act as more of a general guideline. There are some scenarios in which some smell types blend together such as long methods and overly large classes to name one. Detecting code smells isn’t going to always be the same in that what someone is looking for will differ every time and the outcome will also differ every time. This process is kind of like trying to find freshly spoiled food; sometimes it looks fine and sometimes you won’t know what exactly you’re looking for. But what you do know is that something smells off and you probably don’t want to risk the chance of getting sick.

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

This post today is about AngularJS based off of the article AngularJS-Overview by TutorialPoint. AngularJS is a structural framework for dynamic web applications that was developed in 2009 by Misko Hevery and Adam Abrons. Its latest version 1.2.21 is now maintained by Google. AngularJS lets you use HTML as a template language and then allows you extend the the syntax to express application components. It is a framework used to build large scale and high performing applications that are easy to maintain. General features include that it is open source and licensed under Apache license version 2.0, it is an efficient framework able to create Rich Internet Applications (Web apps that are able to perform the normal features and functions of normal applications), provides an option to write client side applications using Javascript under an efficient Model View Controller method, and AngularJS is cross-browser compliant so that it automatically handles JavaScript for each browser. The advantages of AngularJS are that it has the ability to make single page applications in a clean way, provides data binding capabilities to HTML giving it a responsive experience, it is unit testable, uses dependency injection, gives the ability to provide more functionality with less code, and it can run on all major browsers and smartphones. The disadvantages are that it is not secure and not degradable. Since it is a framework that is JavaScript only, it is not safe so server side authentication is a must. Also since it is all JavaScript, if the user disables it, the webpage would show nothing but the basic page.

This relates to class because we were looking at the process and components that go into web applications. I selected TutorialsPoint because I have used it before for overviews, tutorials, and references. It has proven to be a reliable source to me so I do not have to worry about getting the wrong information. Their reviews online are typically very well rated with key words such as reliable, useful and helpful. I have found those three keywords to be true with this article. I had learned many things since I did not know much about AngularJS prior to reading the article. Some of these things include that it is very useful for single page applications and that it is not very secure. This is very useful for me to learn about because I am currently planning a single page web application project so it is an option for me to utilize.

Source: https://www.tutorialspoint.com/angularjs/angularjs_overview.htm

From the blog CS@Worcester – Austins CS Site by Austin Engel and used with permission of the author. All other rights reserved by the author.

Today I will be writing about Maven using information from the article Maven-Overview by TutorialsPoint. Maven is a project management tool giving a complete build process framework. Mavens objective is to provide a comprehensive, reusable and maintainable model for projects and to provide plugins and tools to interact with this model. Since Maven uses a standard directory layout and default build lifecycle, development teams can quickly set up their project builds. When you have multiple development teams Maven can create a standard build structure for all teams to use. This promotes consistency across the project while developers create reports, builds and testing automation setups. Maven gives a way for developers to manage builds, documentation, reporting, dependencies, SCMs, releases, distribution and mailing lists. Mavens project structure is declared in in the pom.xml file. POM stands for Project Object Model and contains the fundamentals of the entire Maven system. This pom.xml file is similar to the build.gradle file we have seen/used in class. Some features of Maven include its ability to generate a website or PDF including complete documentation, backwards compatibility, automatic parent versioning so that there is no need to specify in the sub module, parallel builds achieving performance improvements of up to 50%, and improved error reporting.

This topic relates to class because it is a software project management tool, which can be very useful to developers. We often used Gradle in class which is a similar concept. I selected this article from TutorialsPoint because it is a useful website for quick references, overviews and tutorials. It is similar to w3schools which is another website I use often for quick tutorials, references, etc.. I would say that the information is reliable, just more shallow as it does not dive deep into topics. I would not use it as the only source when trying to learn a certain subject in depth as you will not get a deep understanding of the material. As an overview though, I would say it is perfect. I personally had an okay understanding of Maven as I had used it a couple of times but never knew the finer details as to how it is different from other project management tools. Some things I had learned from this article are its many features including its use for reporting and documentation. I plan to use project management tools for future projects, so it is good to know the differences and know which one is the right one to use.

Source: https://www.tutorialspoint.com/maven/maven_overview.htm

From the blog CS@Worcester – Austins CS Site by Austin Engel and used with permission of the author. All other rights reserved by the author.

https://thevaluable.dev/dry-principle-cost-benefit-example/

I’ve chosen to talk about a very simple design principle this week called “Don’t Repeat Yourself”, DRY for short. It’s a topic that is covered in this class according to the syllabus and I chose it since its similar to another topic I covered YAGNI in that they’re easy to understand design principles. The meaning is quite self-explanatory, don’t repeat code since if you need to change the behavior of a certain project, you’d have to rewrite numerous lines of code if DRY wasn’t applied. However, applying this principle to everything isn’t efficient either. Trying to apply DRY everywhere causes unnecessary coupling and complexity which also leads to more difficulty changing behavior. According to the link above, using DRY means emphasizes knowledge above all else. What it means by that is that when designing and constructing software, one should have the knowledge of when to apply DRY and when not to for the sake of the readability and efficiency of the code.

In that context, DRY reminds me of the importance of resources in a computer when running something. If you want something run faster, you’ll have to sacrifice more memory. And when you want something to have more memory, you’ll have to sacrifice more speed. Both are valid choices; it really depends on the needs of the software and the user. Say for example, I want to create a program that lists all numbers from one to 100 which I do by writing out a thousand lines of code in which each prints a number. However, if I implement DRY, then I could create an integer variable r and a loop which prints the value of that variable and increments it by one until r equals 100. But the case where I want to alter the behavior to list every odd number between one and 100 is where thing get a bit interesting. In the DRYless scenario, I can simply delete the lines of code that print even numbers. Yet in the scenario with DRY, I’d have to add a condition that skips printing variable r for that iteration of the loop. You have to sacrifice complexity for readability and vice versa. Though the choice is ultimately up to what works best in the current situation. Like in my example, the added complexity from DRY doesn’t really harm the readability, in fact it improves it in some ways, at least in my opinion.

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