The section “Be The Worst”, found in chapter four in Apprenticeship Patterns by Dave Hoover and Adewale Oshineye focuses on situations where you aren’t able to learn much from your environment. Being on a strong team has its benefits, other members can cover areas where you are weak on and catch you before making mistakes among other things. Ideally, a team member should be able to take a step back from their team to accurately assess their skill and knowledge. In the case where the gap in skill or knowledge between yourself and other team members in vastly in your favor, then it’s likely that you won’t be able to grow much as a software developer. Because of this, it’s best to start out as the weakest member of a team, hence “Be The Worst”, in order to have room to learn and grow. Emphasis on “start out”; the weaker members of a team should work more to catch up to the rest of their teams. If they don’t bother, then the “Be The Worst” pattern kind of loses its whole point.

Fortunately, or unfortunately depending on how you look at it, I’ve never felt that I was the strongest in a team skill wise. Some of the people I’ve worked with on a team do things super quickly so I had to adapt by starting tasks early and refamiliarizing myself with certain concepts. I guess you could consider that a method of catching up with other team members. In a team setting, I don’t actively try to “Be The Worst” but I at the very least understand why it’s a pattern. If people aren’t challenged, then they’re tempted to stagnate and it becomes all too easy to end up as a big fish in a small pond. When someone better inevitably comes along, then those who’ve done nothing to improve are thrown for a loop and can’t easily adapt to that change. Though, people could also be motivated to do the opposite; work catch up or even surpass their peers in order to not be seen as the weak link.

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

The section “Rubbing Elbows”, found in chapter four in Apprenticeship Patterns by Dave Hoover and Adewale Oshineye focuses on a learning technique similar to “Record What You Learn” pattern which I wrote about during week 12 of this semester. To “Rub Elbows” in this context means to sit next to a fellow software programmer and exchange ideas. This could range from simply talking to each other during a lunch break to peer programming which is when two or more developers physically sit side by side and collaborate while they develop software. In any such case as these, a software developer will likely pick up on the small micro-techniques that are too trivial to be covered during active teaching. However, these techniques can add up and significantly contribute to the development of a software programmer. And when even if one were to disagree with their peer’s methods, they at the very least have gained a new perspective to view from. The same can be said in the case where there’s a knowledge gap between peers, which forces both of them to take each other’s viewpoints into consideration.

Again, like the “Record What You Learn” pattern, I’ve experienced and used “Rubbing Elbows” to an extent. In recent memory, me and a peer have helped each other study and do the work in classes that we share. We weren’t able to physically meet up since the pandemic but we occasionally talk to each other online through discord. I remember one conversation where he mentioned Python and I didn’t really know much of the language so I asked him what separated that with programming languages like C and Java. That got me interested enough to the point where one of my final projects for this semester partly uses Python code. There’s also a pet project that I’m toying around with, something to do with chess; I’m kind of tempted to research more of Python so I can write the code entirely in that language. It’s not what I’d describe as a micro-technique, but it’s something that I picked up while “Rubbing Elbows” with a peer.

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

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.