Forward Engineering vs Reverse Engineering

 

Introduction:

The process through which a detailed model is transformed into executable code is termed forwarding engineering. It is also known as Renovation and Reclamation. After converting to code, much information including small and in-depth details are lost because till now no programming language is richer in context as compared to UML.

On the other hand, as the name suggests, reverse engineering is the opposite of forward engineering. In reverse engineering, the application is broken down into different small modules to extract knowledge of its architecture. It often takes less time to develop. [1]

Now we have a basic concept about forward and reverse engineering, we can dive into a detailed discussion with reference to UML.

 [2] Unified Modeling Language (UML) has been the standard notation for modeling object-oriented software systems since  1997. UML diagrams are important for maintainers of a system, especially when the software was developed by a different team. [2]

Unified Modeling Language:

  Unified Modeling Language, is a standardized modeling language containing an integrated set of diagrams, developed to help system and software developers the undertaking, figuring, building, and documenting the pavements of software systems. UML provides its users with a ready-to-use, extensive visual modeling language so they can develop their business requirements in globally accepted standard notation. In addition to this, it provides broad scope and specialization mechanisms to elaborate the core concepts. It doesn't bound its user to stick with some particular programming language or tool. It provides support to multiple programming languages and tools which will be discussed later.

[3] UML can be categorized as:

1) Structure Diagrams

2) Behaviour Diagrams

Structure Diagrams:

Seven types of structure diagrams are as follows:

Class Diagram

Component Diagram

Deployment Diagram

Object Diagram

Package Diagram

Composite Structure Diagram

Profile Diagram

Behavior Diagrams:

Different types of behavior diagrams are as follows:

Use Case Diagram

Activity Diagram

State Machine Diagram

Sequence Diagram

Communication Diagram

Interaction Overview Diagram

Timing Diagram [3]

Matrerial and Method:

   Tools:

·         Microsoft  Visual  Studio is highly  common  graphical  IDE  with support for C, C++, C#, and VB.[3]

        • Rational  Rose,  a  specialized  modeling  tool  for  system  architectures based on UML, included for manual UML-modeling and as “standard” only. Unfortunately Rose does not support neither forward nor reverse engineering support for C #. [3]

        • PowerDesigner v11.0, a tool for modeling of data systems with the aid of UML.[3]

        • Visual  Paradigm  v2.1.  VP  SDE  is  made  for  UML-modeling  and  integration  with  Visual  Studio,  but  lacked support for the 2005 version of Visual Studio. It was therefore tested together with the installation of MS Visual Studio.[3]

·          Gliffy is an online cloud-based UML tool for browsers. It includes all types of diagrams such as flowcharts, Venn diagrams and UML diagrams. This tool is suitable when you are working in teams. It is a premium tool i-e after 14 days free trial, user need needs to pay for certain features.[4]

·         ArgoUML has been one of the most prominent open-source UML desktop applications. It is no longer maintained widely nowadays due to its limited scope but it is still used for smaller projects. ArgoUML supports a considerable range of programming languages whose code can be generated from a diagram. Reverse engineering is also possible for Java, C++, PHP, C#, and SQL. The program also recognizes other languages such as Delphi or Ruby when you add them to the ArgoUML file folder as extensions.[4]

UML-Modeling:

Class Diagrams are created according to provided data of classes, methods and return types. User can add classes, methods, parameters etc. through drag and drop from relevant menus. Talking about Visual Studio in particular, it has less functionality of other UML diagrams as compared to Class diagram. The reason is that Visual Studio Is an IDE and is designed for representing the models in notation that is closely related to executeable code. Visual  Paradigm is integrated into an IDE (Visual Studio), which allows full scale modeling  and  programming  within  the  same  environment.  Code  generation  is  done  in  real-time  in  Visual  Paradigm and Visual Studio. [2]

 UML-based code generation:

 Out of all mentioned tools above, Visual  Studio,  PowerDesigner  and  Visual  Paradigm  supports  C#  generation. Standard data types are also supported in generated code. “DataSet” and “ArrayList” are treated as especially defined data types. Microsoft Visual Studio automatically  generates  the main  method,  which  adds  the  class  “Program”  that  contained this method.  Visual    Studio    and    Visual    Paradigm adds:    “throw new    System.NotImplementedException()”,   VirtualParadigm   SDE   adds:   “throw   new   System.Exception(“Not   implemented”)” while PowerDesigner only added the comment: “//TODO: Implement” .  [2]

Reverse Engineering:

 Visual  Paradigm did not  perform  the  UML-generation  due  to  an  internal  error, and not able to save the file in a correct format. t  Microsoft Visual Studio  presents  a  correct  class  diagram  after generating UML diagram. Furthermore, PowerDesigner is not able to generate 'modifier partial class' in class  diagram rather it generates an error message stating that PowerDesign doesn't support this functionality. [2]

Results:

An experiment was performed to test whether the use of UML influences maintenance in contrast to the use of only source code. The experiment was conducted between 20 professional software developers. Results showed that those who received UML diagrams require more time in code generation. UML also helped in generating efficient code when information about the scope of the system was limited. [5]

     Another experiment was conducted among 16 undergraduate students in order to test whether the comprehension of source code increases when participants are provided with UML class and sequence diagrams produced in the software. Results showed that participants comprehend source code better when it is added to class and sequence diagram together. [5]

A survey was conducted to check the amount of practice required to use UML in software development. A significant number of people participated in that survey. Results revealed that companies interviewed the people without keeing in view their proficiency in UML and actually intermediate level of expertise are required for using UML in software development. [5]

Conclusion & Discussion:

Different tools are tested for performing forward and reverse engineering. The modeling equipment which have been examined show, as expected, enormous similarities withinside the ensuing code and UML-diagrams. The finest variations are the ones connected to the individual tool’s fundamental focus. Microsoft Visual Studio is especially a software program improvement surroundings and as such does it lack a few of the state-of-the-art modeling techniques. Microsft Visual Studio 2005 .NET beta is likewise the primary model of Visual Studio that embody any modeling capability, and it best helps elegance diagrams (different modeling talents does not observe the UML popular and lays out of doors the scope of this article). Visual Paradigm differs from the fundamental flow modeling equipment in that experience that it's far integrated with Microsoft Visual Studio.

Unified Modeling Language is one of the fundamentals of software development. Before the use of this technique, the maintenance of code was literally a headache. Performing a change in even a small portion of code and handling that change was an uphill task. Since 1997, since the introduction of UML modeling, code maintenance has been improved and become efficient. UML provides complete understandings of business flow, software's key functionalities, application tiers, and integrated environment. For instance, if a certain level of the software engineering process, a change or update is required in the code or implementation model, we have every phase clear in front of us. We can pinpoint that particular area without disturbing the whole model.

 

Referrences:

1.    https://www.geeksforgeeks.org/difference-between-forward-engineering-and-reverse-engineering/#:~:text=In%20forward%20engineering%2C%20the%20application,collected%20from%20the%20given%20application.&text=Forward%20Engineering%20is%20high%20proficiency,engineering%20is%20low%20proficiency%20skill.

2.    https://www.researchgate.net/publication/276144909_A_Comparative_Study_of_Forward_and_Reverse_Engineering_in_UML_Tools

3.    https://www.visual-paradigm.com/guide/uml-unified-modeling-language/what-is-uml/

4.    https://www.ionos.com/digitalguide/websites/web-development/the-best-uml-tools/

5.    https://www.researchgate.net/requests/r80523810

SCOPE OF TESTING FOR ARTIFICIALLY INTELLIGENT SYSTEMS

 

INTRODUCTION:

                               Artificial Intelligence means human-like behavior shown by computer systems and software. It means that application or machine can understand human behavior dynamically and can adapt a certain behavior according to the situation.

 Artificial Intelligence (AI) traditionally speak of to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language. These behaviors allow AI to bring vast socioeconomic prospects, while also posing ethical and socio-economic experiments. [2]

The term itself was coined by Dartmouth College’s John McCarthy in 1955 in a proposal to university researchers for its summer research project on AI. According to the cognitive scientist Marvin Minsky, one of the field’s most famous practitioners, AI is “the science of making machines do things that would require intelligence if done by men.” [1]

Practically, artificial intelligence represents a wide category of methodologies that teach computer to adapt the behavior of an ‘intelligent’ human. Two terms “Machine Learning” and “Deep Learning” are frequently used and seen when we discus about artificial intelligence. Machine Learning refers to the algorithms that are developed to tell a computer how to respond to a certain event or activity. Deep Learning is the type of machine learning which use the structure that is closely related to human behavior. Deep-learning software attempts to simulate the activity in layers of neurons in the neocortex, the wrinkly 80% of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data. [1]

Artificial intelligence has created a new revolution in the world of business and information technology. Through artificial intelligence, our tasks have become frequent, accurate, bug-free and effortless. Many of those tasks which required human presence and efforts are now done by software and hardware tools using artificial intelligence. One of the best examples in this perspective is plagiarism detection software. Before the use of artificial intelligence, humans used to check for the plagiarism manually. But with the evolution of technology and introduction of AI, there have been a large number of software that detects plagiarism within seconds of unlimited word count. 

Even if artificial intelligence induces thoughts of science fiction, artificial intelligence already has many uses today, for example:

·         Filtering: Email and text-messaging services use artificial intelligence to filter incoming messages. User can mark a particular contact as ‘spam’ and next time that contact tries to send message, your machine will automatically detect it.

·         Personalization: Artificial intelligence has played a vital role in the world of sales and advertisement. Online services like Amazon, Daraz, Foodpanda, Netflix etc. learn from user’s previous experiences and display him the related content.

·         Fraud Detection: Banks implement specialized algorithms that detect if there is some strange activity and report it instantly.

·         Speech Recognition: Applications use artificial intelligence to optimize speech recognition functions. One of the most common examples is assistant feature in our mobile phones. [2]

 

 Everything has its bright and dark sides but it depends on how we perceive it. Following are some pros and cons of AI:

Advantages:

·         By the use of AI, human error is reduced. Everything is programmed according to specialized algorithms and codes and greater sense of precision is achieved.

·         Artificially intelligent system is able to take risks because he got no emotion attached. A human being may think for a moment before defusing a bomb but a robot doesn’t.

·         Human body needs rest. On average, a human can work for 8-9 hours continuously. But services which are provided through artificial intelligence are available 24x7.

·         Digital assistance is one of the most useful services provided through AI. Even if relevant persons are not present at customer support center, a user still gets response by Chabot.

·         Using AI combined with other technologies we can make machines take decisions faster than a human and bring out activities quicker. While taking a decision human will evaluate many factors both emotionally and practically but AI-powered machine works on what it is programmed and provides the outcomes in a faster way. [3]

 

Disadvantages:

·         Development of AI based application requires a lot of resources including human efforts, expensive hardware devices and latest technology.

·         AI is making humans lazy with application automating majority of the tasks.

·         The use of AI has created unemployment at larger scale. Robots have replaced the man at many important jobs and this risk is increasing day by day.

·         An artificially intelligent system lacks out of the box thinking due to the algorithms build on hard and fast rules. If any unexpected event happens, that system is not able to respond correctly.

 

MATERIALS & METHODS:

It is commonly believed that AI has become as mature as human brain. But the truth is that the scope of problems solved by AI is narrow. AI based system cannot learn by their own. They are accomplished to implement a specific task and support humans in solving demanding problems. AI can also go wrong so we need to apply different testing techniques to ensure correct usability. Both SDLC (Software development Life Cycle) and STLC (Software Testing Life Cycle) are different for artificially intelligent systems. The reason is that AI involves training the machines to acquire, unlearn, and adjust definite tasks throughout their lifespan; there is a particular need to train and test them properly. Moreover, intelligent machines have machine learning skills that will result in different results if the same function is called at different instants in time. This demands new testing skills.  [2]

·         Black Box Testing: In this type of testing, software is not tested on the basis of coding structure. Rather, main focus is on certain types of input and the expected outputs that are based on software requirements. This is also called as Behavioral testing. [2]

·         A/B testing: Many software companies practice A/B testing. They provide two different versions of their software and investigate the user’s reaction. This method is based on the scientific process and should be utilized when developing AI aptitudes.[6]

·         Machine Learning and Neutral Networks: A neural network comprises of many lumps of “nodes” which are arranged in layers. All the nodes are interconnected. These nodes are not pre-programmed rather they are empty. Their processing parts are presented in top layers where calculated result is returned. When the neural network is exposed with an example in training it will analytically construct itself so the different layers and nodes will process parts and aspects of the input so the end result of all nodes will give the result that is given to the network i-e label.[2]

·         Specialized Algorithms: In normal systems, algorithm means coding techniques applied to it. But in the case of artificially intelligent systems, the algorithm is the product of the neural network but based strongly upon the training data and the labels. So the algorithm is equal to code + training data + labels. [2]

·         Specific Input Testing: Different types of input values can cause a mix of expected and unexpected outcomes. The input values are important for the functional reliability, security, strength and performance. An artificially intelligent system keeps on improving as more and more data is provided as input. So this cause the system to change the behavior as it proceeds. An AI tester should keep that fact under special consideration. [6]

·         Test Oracle Problem: Test oracle problem occurs when tester is not sure about expected result of specific use cases. It becomes really confusing to determine whether the actual results are according to expected results or not.[6]

RESULTS AND INTERPRETATIONS:

Due to limitations in training data and concepts, we see things too simple (reductionism) or only from one point of view (bias). A granularity in the concepts could mean that the system cannot generalize enough, thus the result is useless.

·         Selection Bias: If the training data in the selection input has some important points missing, then it can lead to the situation of selection bias. Consider an example of mood-based recommendation system. If the information given to such a system is incomplete, the results could be far away from expectations.

·         Confirmation Bias: Willingness to verify Associate in attention hypothesis heavily believed or invested with in will lead to choosing or over-weighing information confirming the thesis over manageable misrepresentations. Scientists, politicians and merchandise developers might be prone to this type of bias, even with the simplest of intentions.

·         Under-fitting: As we have learned earlier, artificially intelligent system improves itself as more and more training data is given as input. If training data is not enough, the system may lack many of its functionalities. For example, a system whose job is to differentiate between the voices of different animals. If you don’t provide voices of all the animals as training data, how can it respond according to expectations in a real situation?

·         Over-fitting: Over-fitting  happens  when  the  classification is too miscellaneous  and  too  assorted  for  the purpose of the AI system. If we want to see designs and groupings, a high granularity of labels compromises the outcome, making it impracticable because of its vagueness.

·         Over-Confidence in AI system: AI can perform some types of mental activities way faster and accurate than a human brain can. The algorithm of AI is not directly reachable or modifiable. From this the perception can be easily drawn that AI cannot be judged by human standards. Intellectual laziness and comfort can be an important motivation too for uncritically trusting AI. Take the example of Google search. Human brains of thousands of people cannot match the speed and accuracy of Google. Therefore, nobody questions the result and algorithm behind it. [2]

DISCUSSION & CONCLUSION:

We’re fast oncoming a time when even “Continuous Testing” will be incapable of keeping pace with declining delivery cycle times, increasing technical complexity, and rushing rates of change. We have already starting to use basic forms of AI, but we need to continue the testing evolution to achieve the efficiency needed for testing of robotics. We need to learn the importance of smart work. Hard work alone is not sufficient always.  Certifying quality in an era where software will be processing an unbelievable number of data points in real time. For example, now a day self-driving cars are about to introduced. Definitely, the car will perform in real world. This means more complexities are on the way of testing and quality assurance. Particularly, we can’t afford even a slight error in this regard because it is not about failure of certain functionality; it’s a matter of lives. So we need to keep pace with the upcoming technology and should improve our testing standards. [6]

Another fear about artificial intelligence is that in near future, it will completely take the role of humans and there will be too much unemployment. This is true to a little extent but we don’t need to be worry. As we have seen, when technology replaced the old conventions, new opportunities also arrived. So we should look at the brighter side and should try to adapt according to modernization.

 

REFERENCES:

1.       https://expertsystem.com/artificial-intelligence-software-definition/

2.       https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-learning-policy-paper/?gclid=CjwKCAiAqJn9BRB0EiwAJ1SztUWJokzWqk6tzsmhV0DO5mnzocyn9tSxCfGx8_4BJA77PgJWPnmjIhoCwkwQAvD_BwE

3.       https://towardsdatascience.com/advantages-and-disadvantages-of-artificial-intelligence-182a5ef6588c

4.       https://testaing.com/testing-artificial-intelligence-ai-systems/

5.       https://www.researchgate.net/publication/337400746_Testing_Artificial_Intelligence

6.       https://labs.sogeti.com/wp-content/uploads/2017/12/Testing_of_Artificial-Intelligence_Sogeti-Report_11_12_2017.pdf

7.        

 

Free Logo Designing Tools: Start making money online

 

Free designing tools

 

In today’s world, finding something for free is really an uphill task. But if you do so, give yourself a pat for a really amazing job. Designing is considered as one of the necessary techniques of business as colors and graphics play an important role in correct reflection of your brand towards your customers. So one should know the basics of designing and graphics. For basic practice, some free tools are available in the market which are categorized as follows;

Free Logo Designing Tools

Logos represents and reflects your brand and product and it is an essential part of the business. Some famous and handy logo designing tools are given below:

·         Brand Crowd

·         HOTH

·         Tailor Brands

·         Canva

Tailor Brands: -

It is one of the easiest logo designing tool available online. If you are not very good at graphics and still want to create your logo by your own instead of relying on others, then you are on the right platform. Simply go to tailorbrands.com or download the app. When there, it will simply ask you to enter the text which will we show in your logo. In the next step, it will ask you about the industry about which you are going to make a logo. You can also explain about your industry to make it better and accurate. Then it asks for logo type. You can choose from three types. In the next 4 to 5 steps, it will keep asking for different font styles to make it near your choice. Finally, your logo is ready and you need to log in through your Gmail or Facebook account to download it. That’s it! How simple is that.

Brand Crowd: -

This one is even easier. Simply go to tailorbrands.com or download the app. When there, it will simply ask you to enter the industry for which you want to make a logo. Click next and it will show you multiple logos (almost 30 to 50) of different styles. Select one and there you go. Your logo is ready. See! It is that simple. Now you can adjust your selected logo according to your requirements and needs. At the end, you need to log in from your Gmail or Facebook account to download your account.

HOTH: -

This is surely the most efficient of all of the above as it is free-hand designing tool. Different business logos are available by default but still you can create of your own choice. But it is comparatively difficult to use and requires some expertise.

 

Canva: -

Canva is one of the most used graphic designing tool worldwide. It is suggested to a person, who is new to the field of graphic designing, should start from Canva. It provides you with a wide range of designing options. There are many free templates available, and using these templates, you can design attractive logos, business cards, flyers, brochures, social media posts, presentations, announcements, thumbnails, website designs etc. Some of the features require premium version, but majority of the elements are free to use.
 

                                   To summarize, the logo represents your brand’s identity and is a crucial part of ventures and business. In today’s world, where the trend of entrepreneurship is rising day by day, the job opportunities for graphic designer, more specifically logo designers are also increasing.
If you got any queries left, you can ask in the comment section. 

 

Top digital skills to learn in 2021: Make money online

 

In this modern age of technology, the importance of digital learning cannot be denied. Millions of people all over the world are earning on a daily, weekly, and monthly basis by providing their services online. One of the major benefits of online jobs and services is that it does not require a person to travel from one place to another and saves a lot of time and resources. You can provide your services and connect to the client from anywhere. You just need a reliable laptop or smartphone and a stable internet connection. Especially, in this pandemic situation, a lot of people who were running businesses physically, have shifted to the online mode. Moreover, the rate of unemployment has also increased due to major lockdowns. Those people who lost their jobs are looking for something that provides them earning. Besides that, people with jobs can also learn digital skills and provide services as their part-time job. This article is all about the skills which have a wide scope in 2021 and further coming years. Learning these skills can highly beneficial if you want to earn online.

• Virtual Assistant

Right now, this is one of the most demanding skills on different freelance marketplaces including Fiverr, UpWork, Guru, etc. Two tools that you need to master necessarily to become a virtual assistant are Microsoft Word and Microsoft Excel. Microsoft Powerpoint is a plus. Since there is a lot of competition for this skill, so you have to be perfect and fully trained. Don't preassume that typing and data entry are simple tasks and can be done easily. No, there are smart ways you need to learn that make you able to perform lengthy tasks in less time. YouTube is the best platform to learn it. Start taking tutorials and practice it side by side.

• Graphic Designing

Due to the positive increasing trend of entrepreneurship, new startups and ventures have started establishing themselves. Every new startup needs to have a brand identity so they require a graphic designer to make logos and visiting cards for them. This skill has really a vast scope right now and in the future as well. There are different tools for graphic designing including Adobe Illustrator, Photoshop, Vector, Canva, etc. The simplest of them is Canva and it is recommended to start learning on Canva and then to upgrade yourself on some other tool.

• Project Management

Project management is the technique to keep track of different project deliverables, their dependencies, sequence, time and cost estimation, etc. Many internationally reputed companies are hiring project managers remotely so that they can keep track of their project activities. You need to learn and master project management tools such as Microsoft Project, Podio, ProofHub, etc.

• Search Engine Optimization

  Search Engine Optimization (SEO) is the technique to improve the ranking of the websites on the search engines. Using this technique, you can rank your website or pages among top searches so that you may attract more visitors.

With the increasing trend of online businesses, most startups have their own website. So those who have a website, definitely require an SEO expert. This skill is really in-demand and has a broad scope in the future.

• Video Editing

      Every business or venture needs means and ways to introduce and endorse themselves. Video ads are the most common way to advertise your products or services. So companies are in search of professional video makers who can convert their written ideas to motion graphics. This is also a highly demanding skill and has a bright future.

To summarize, learning new and trending skills is highly important especially for those who are struggling financially. You are not limited to these five skills only. There are many others. But I find these five with broad scope and a bright future. 

If you have any confusion or question regarding this, you can ask in the comment section. Thank you so much for giving it a sight.

Sequence Activities : Project Management

 

Project Scheduling is creating the lists of activities, deliverables, and milestones within a project. These activities and deliverables need to be in a certain format and sequence in order to correctly estimate the time and resources required to complete a project. 

Sequence Activities help us in identifying and documenting relationships among the project activities. The main advantage of this activity is that it defines the logical sequence of work to obtain the highest possible product within the scope of the project. The major techniques and tools which are used to apply this process include the Precedence Diagraming Method (PDM). Sequence activities are usually of four major types:

Start to Finish:

In this dependency, there is a definite association between the start of one activity and the end date of a successor activity.

Example: Consider a scenario where you have to conduct a cricket match. Before the start of actual play, a coin is tossed to decide which team is going to bat first.

Activity A: Tossing of a coin

Activity B: Start of the play

You cannot start the actual match before the toss. So when activity ‘A’ (toss) is finished then activity ‘B’ would start.

Start to Start:

There is a distinct connection between the starting of both the activities. However, the completion of both activities is independent.

Example: In Software Development Life Cycle (SDLC), as the software team is gathering requirements from the client, the graphic designer or frontend developer starts developing wireframes and prototypes.

Activity A: Requirement gathering

Activity B: Development of wireframes and prototypes

Both the activities go side by side.

Finish to Start:

Finish-start dependency means an activity ‘B’ cannot be started before the previously started activity ‘A’ has ended.

Example: Consider the example of a metro bus service. In the beginning, the tracks and bridges are constructed. In order to test the bus service, the construction of tracks needs to be finished.

Activity A: Construction of tracks

Activity B: Testing of buses

Finish to Finish:

In this type of dependency, both the activities are ending at the same time however starting time of these activities can be different.

Example: As we know, unit testing is performed by the developer. During the unit test phase, if a bug is found, the developer fixes that bug and performs a test for it. This process is continued as long as the last bug is removed. As there is no bug, the process of testing is ultimately stopped.

Activity A: Bug identification

Activity B: Testing