Web application analyst – table of contents:

1. What are the tasks of a web application analyst?
2. Sample tasks of an application analyst
3. What qualifications do you need to become an application analyst?
4. Skills needed to work as a web application analyst
5. Is work experience necessary for the application analyst profession?
6. What are the salaries for an application analyst position?
7. Opportunities to work as a web application analyst
8. Application analyst’s work environment
9. How to become a web application analyst step by step

What are the tasks of a web application analyst?

A person who works as a web application analyst is part of the IT team. It is worth noting here that in each organization this position includes slightly different tasks, depending on the needs of the company. However, most often a person who works as an application analyst deals with the analysis of the existing system, detects errors as well as works on its development and improvement. Thus, in a nutshell, the application analyst takes care of the application as a whole and monitors its development. Additionally, he or she looks after the audience of an application, more specifically, whether they have access to the right data and information.

Sample tasks of an application analyst

It is impossible to clearly define all the duties that will belong to a web application analyst. However, the following collection of examples of the most common tasks will give some idea of the specifics of work in this profession:

• Analyzing the needs of the organization in terms of application needs
• Performing analytical research on the company’s existing applications
• Streamlining existing applications, performing updates
• Running education of user application processes
• Overseeing the implementation of new applications in the company
• Constant monitoring of reported errors, their analysis
• Technical work related to the server
• Responding to problems and ensuring continuous improvement of the system

What qualifications do you need to become an application analyst?

Nowadays, there is a growing shift away from checking the skills of employees through their academic degrees, which does not mean that they are not welcome. A person with a degree in IT already has some structured knowledge, which is crucial. People who have a degree in computer science, engineering or information technology, but also in analysis or mathematics, will be welcome in this position.

However, more important than education alone are the technical skills an applicant for a web application analyst position possesses, as well as the experience he or she has.

Skills needed to work as a web application analyst

Technical programming skills are required for this job. Depending on what computer language the company supports, you may need appropriate certifications such as CSS, HMLS or others. Experience appears fundamental as theoretical knowledge alone is not enough. An application analyst is required to keep getting updated with new technologies and be able to react quickly to a shifting work environment.

In addition to purely technical skills, character traits and personal skills are also essential for this job. Application analysts have to work in a team. They have to communicate and convey factual information and data so that it is correctly understood and read by the other members of the team including those non-technical users of the application in question.

Contrary to appearances, this is a crucial skill. Unfortunately, not everyone possesses it. Even the best specialist in the field who can’t work together in a team may not find their way into the position.

The information analyst should also have a basic knowledge of project management and appropriate leadership skills. Chaos can creep into an inadequately managed team, which will result in delays, missed deadlines and mismanagement of human resources.

Is work experience necessary for the application analyst profession?

A person who has a degree in the field and experience in a similar position is the ideal candidate. However, this does not mean that other candidates do not get hired. Many companies offer free internships or apprenticeships so that interested people can gain experience, see if the job is for them and, if they prove themselves, even get a job.

It is known, free or practically free labor is not an ideal solution. However, if you want to work in this position, you should treat it as a transitional stage and an opportunity for growth. Take this time to learn, ask questions and try to get the most out of your internship.

What are the salaries for an application analyst position?

It is impossible to clearly define the level of salary an application analyst receives. It all depends on what country, what experience he or she has, and what additional skills or certifications he or she has.

Wanting to see how much app analysts earn in a specific geographic area? All you need to do is look at the job portals. Most listings give the monthly earnings for the position.

IT workers are in demand in the job market today, especially those who are experienced and highly qualified. Employers fight for such people and try to offer them the best possible working conditions. In addition to a high salary, they can count on additional bonuses like private insurance, life insurance or other various amenities.

Opportunities to work as a web application analyst

The demand for application analysts is still very high. This is one of the professions of the future. So it is a good direction for people who are just considering what career path to follow and have the desired personality traits.

Virtually every industry is automating processes and moving to “new technologies.”

Application analysts work with different sectors depending on where the software is deployed. These include government as well as healthcare or public institutions.

Application analyst’s work environment

Although an application analyst mostly only needs a computer to work, he or she often needs to be in the organization or some branch of it. This means that, depending on the size of the corporation, the job may also involve business travel.

As for the mode of work, it is mostly billed on an hourly basis and analysts work during standard office hours, but not always. Some tasks require a project-based approach, where it is not the working time that counts, but the result. So this may mean that an application analyst in some situations will work longer hours or at the weekends.

How to become a web application analyst step by step

If, after reading the above description, you still want to become an application analyst and think this is the career path for you, you’re probably wondering where to start.

As you know for this job you need specific technical skills, certifications and completed courses. So start there. Check the ads in the area where you want to work and see what specialists are most in demand. This is a good hint about what operating system or programming language to start with. Focus on one and develop your skills in it. When you complete the course you can apply for an internship with the company. This will give you the practice you need for this profession, and perhaps even employment.

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Verification and validation in the software testing process – table of contents:

1. What is verification in software testing?
2. What is validation in software testing?
3. Features of the validation process
4. Features of the verification process
5. Key differences between verification and validation

What is verification in software testing?

Verification in software testing is the process of checking documents, design, code and program to control whether the software has been built according to requirements. The primary role of the verification system is to guarantee the quality of the application, architecture, design, etc. As part of the software verification process, activities such as reviews here are undertaken.

What is validation in software testing?

Validation in software testing is an effective mechanism for testing and verifying that software adequately meets customer needs. Thanks to this mechanism, it is possible to assess whether the software fulfills its purpose in a suitable environment. During the validation process, activities such as unit testing, integration testing, system testing and user acceptance testing can be performed.

Features of the validation process

The main features of the validation process are as follows:

• it is a dynamic mechanism for testing and validating the actual product
• it always involves code execution
• it uses methods such as black-box testing, white-box testing, and non-functional testing
• it can detect bugs, failures and other inaccuracies that the verification process cannot catch
• the target is the actual product
• it is performed with the help of a qualified team of testers
• checks if the software meets the customer’s requirements and expectations
• follows verification

Features of the verification process

The main features of the verification process include:

• inspecting documents, design, code and program
• it does not require code execution
• checks if software conforms to specifications
• finds errors early in the development cycle
• the target area here is the application and software architecture, complete design, specification, high-level design, and databases
• it is carried out by the QA team, which makes sure that the software complies with the requirements in the SRS document
• it uses methods such as instruction, reviews, inspections, and document checking
• verification happens before validation

Key differences between verification and validation

During the verification process, documents, code, design and program are checked, while validation is already based on testing and validation of the real product.Verification does not involve code execution, while validation does.

Verification uses methods such as reviews, guides, inspections while validation uses methods such as black-box testing, white-box testing and non-functional testing. It checks that the software conforms to the specification, while validation checks that the software meets user requirements and expectations.

Verification focuses on finding errors early in the development cycle, while validation concentrates on identifying errors that verification cannot catch.

Comparing the validation and verification in software testing, it can be said that the verification process is about the software architecture, design and database while the validation process is about the actual software product.

Verification is carried out by the QA team, while validation is done by the testing team with the QA team. The verification process occurs before the validation process.To sum up, validation and verification are processes that differ significantly from each other.

The former includes all forms of testing, which determine whether the system meets the customer’s expectations, while verification determines whether the system works properly, according to functional and non-functional requirements. The verification process always precedes the validation process.

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Testing vs debugging- table of contents:

1. What is software testing?
2. What is software debugging?
3. Testing vs. debugging
4. Differences between testing and debugging
5. Summary

What is software testing?

Testing is the process of verifying and validating software. It ensures that it works as intended. In other words, it verifies that the software has achieved the intended result. Testing is carried out by a team of testers. They identify bugs in the software, and when they find them, they record them and inform the development team.

Then, the testers create a comprehensive report containing all the details of the error. They include information such as the identifier, description, occurrence, what happened because of the error, etc. The development team uses this report to fix the bugs. The following distinction can be made:

Functional testing verifies that the software has reached the expected level.

• Unit tests are used to verify that each module works as it is expected. It is the testing of individual modules. Unlike other types of testing, unit tests are performed by developers, not testers.
• An integration test is a test of two modules that checks the flow of data between them.
• System testing is the testing of all software.

These are some common types of functional testing. Non-functional testing checks non-functional aspects of the system, such as performance, scalability, usability, etc.

What is software debugging?

Debugging is performed by the test team after receiving the test report from the testers. The programmer analyzes the reports and tries to find the cause of the errors. The person goes through the fragment of code where the error occurred and checks each line of the program.

After finding the error, they modify the code fragment in question and check again to see if the fault has been corrected. Once the bug is fixed, the programmer sends the software to the test team.Nowadays, most development environments have a special tool called a debugger to assist in the debugging process.

Testing vs debugging

The main difference between testing and debugging is that testing is the process of finding and identifying software defects, while debugging is the process of correcting identified defects.

Software development is a complex process involving several stages, such as requirements analysis, design, development, testing and maintenance. Testing and debugging are two activities involved in software development.

Testing is the process of finding defects in software. Debugging is the process that helps fix identified defects. Both testing and debugging help deliver working and high-quality software to users.

Differences between testing and debugging

Definition

Testing aims at verifying that the actual results correspond to the expected results when it comes to software operation and making sure that the software is free of defects. Debugging is the process of finding and fixing defects or problems in a computer program that prevent the software or system from working properly.

Main task

The main difference between testing and debugging is that testing is the process of finding and identifying software defects, while debugging is the process of correcting identified defects.

Execution

Testing is carried out by the testing team, and debugging is done by the development team.

Purpose

Another difference between testing and debugging is the goal. The purpose of performing a test is to find as many defects as possible. The purpose of debugging, however, is to fix the bugs found.

Way of implementation

While testing can be done manually or automatically, debugging is done manually.

Testing vs debugging – summary

To conclude, both testing and debugging are important activities in the software development process. The difference between testing and debugging is that testing is the process of finding and identifying software defects, while debugging is the process of correcting identified defects.

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6 phases of STLC – table of contents:

1. Phase 1: Requirement analysis
2. Phase 2: Test planning
3. Phase 3: Test case development
4. Phase 4: Environmental setup
5. Phase 5: Test execution
6. Phase 6: Test closure
7. 6 phases of STLC – summary

Phase 1: Requirement analysis

This is the first step in the cycle. The test team carefully reviews the product requirements. If there are any conflicts, omissions, inaccuracies or misunderstandings, the test team discusses them with various project stakeholders, such as the business analyst or software architect.

Entry criteria:

• Business Requirement Specification (BRS)
• a software architecture document

Tasks to do:

• determine the tests to be performed
• set priorities for test execution
• check which tests must be manual, and which must be automated

Exit criteria:

• a list of requirements for testing
• any tests to be automated

Phase 2: Test planning

At this stage, the validation team plans all testing activities by writing a test plan. This document specifies:

• the objectives to be achieved
• the processes and methods to be implemented
• the environment and tools to be used
• the elements to be tested or not tested
• the organization of the team and the division of tasks,
• intermediate goals for various activities
• risks that may arise

Apart from developing the test plan, a cost estimate is also drawn up in this phase.

Phase 3: Test case development

In this phase — also known as Test Design — there are four steps to follow:

1. Prepare test scenarios

The test manager or test leader prepares a test scenario which will be used to create test cases.

2. Create test cases

For each scenario, testers will write test cases so that they can verify that the software functionality meets its requirements. In the case of test automation, it is at this stage that test scripts will be written.

3. Prepare test data

The test team needs to prepare a set of data to be used when executing test cases. This can be positive or negative data to test the function’s performance in the case of correct, or incorrect data.

4. Prepare RTM

The test team prepares a key Requirement Traceability Matrix (RTM). This document is used to keep track of what tests are needed to verify whether requirements will be met or not. Before testing begins, stakeholders will conduct checks and validations of what was developed during the above activities.

Phase 4: Environmental setup

This is a phase in which the test team is not involved. A separate team will handle the preparation and configuration of the environment. Testers will be informed how the environment has been set up and which software version is up to date.

The only activity required of the test team is to prepare smoke tests to verify that the installed build is suitable for testing. If the smoke tests fail, the build will be rejected and testing will be suspended until the indicated problems are fixed.

• test plan
• test date
• smoke test

• test environment preparation
• test environment setup
• setting up test data
• executing smoke tests on compilation

• usable test environment
• usable test data
• positive smoke test results

Phase 5: Test execution

This is simply the execution of tests. During this phase, testers can identify possible anomalies and test the improvements developed by the programmers. The tasks of the testing team will be:

• run the previously developed test cases and compare the expected result with the obtained one
• maintain the test script
• identify, detect, log and report any detected defects
• retest the defect fixes

• functioning test environment
• correct test data
• test plan
• test cases to be executed

• perform tests according to the test plan
• document test results
• manage the defect life cycle

• executing all tests involving the MTR
• updated test cases with results
• defect reports

Phase 6: Test closure

The software will be implemented. The validation team meets to analyze the results and identify areas for improvement in future projects.The test manager prepares a test closure report, which will be done on a daily (DSR – daily status report) or weekly (WSR – weekly status report) basis, as agreed by the stakeholders.

In the end, the test team meets to analyze the test cases, defects found, time spent, compliance with deadlines, etc. In this way, it is possible to determine what needs to be improved in the next test cycle.

• test execution reports
• defect reports

• analyze what has been tested
• create a test closure report

• closing the process without open defects
• test closure report

6 phases of STLC – summary

Software development could not exist without a testing phase. Proper preparation for this process brings a number of benefits, including, most importantly, saving time and money for possible future revisions. We hope this article helped you learn more about software testing life cycle (STLC).

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Digital twins – table of contents:

1. What is a digital twin?
2. What are the main types of digital twins?
3. What is the difference between digital twins and simulation?
4. Main areas of application of digital twins
5. Advantages of digital twins for the industry
6. Future applications of digital twins
7. Threats and concerns

What is a digital twin?

A digital twin is a model in virtual reality that is designed to reflect another physical object that exists in the real world. The digital twin is an exact replica of an object, not just a picture or a scheme, or not even a simulation of the object, but a replica that reflects all the features of the object. The replica or the digital concept that contains the information about the real object can be created and exist as the object on its own and be linked with its physical model permanently.

All this means that the concept of digital twins contains three major elements: real space with the real object, virtual space with virtual objects, and the connection between them that allows a constant flow of information between virtual space and real space.

The idea of using the technology of digital twins was first introduced in 1991 in the publication entitled Mirror Worlds: or the Day Software Puts the Universe in a Shoebox by Yale computer expert David Gelernter. In the publication, Gelernter argues that exact representations of the world accessible through the screen of the computer will be available to everybody in the close future. In his vision, people will be able to interact with mages the same way they interact with the reality experienced every day. Gelernter predicts that computers will be used differently and explains how it is possible to build a replica of the world with today’s technologies.

The concept of digital twins was also developed by professor Michael Grieves, who is credited with the first application of the idea to be used in manufacturing. He presented the concept in 2002 at the event devoted to Product Life Management. Initially, the concept was referred to as the Mirrored Spaces Model. His articles and publications praise the perfection and usability of digital products that are not only indistinguishable from their real models and great for testing and simulation purposes.

However, the technology of digital twins is not as recent as it seems. NASA has used digital twin technology already in the 1960s during their space exploration missions when spacecraft were replicated on earth and used for testing and research. The mere term “digital twins”, as we understand it today, was used by NASA’s specialist John Vickers in 2010.

What are the main types of digital twins?

There are several types of digital twins, but the main difference is in the area or depth of application, hence we can talk about: part twins, asset twins, system twins, or process twins. Part twins are very basic, small twins of working components. Asset twins are used for so-called assets formed from two separate components working together. Asset twins allow observation and measurement of the two components in action.

System twins are on the next level of complexity. Those twins replicate entire functioning systems and allow studying bigger physical objects. Process twins reflect the work of huge systems such as factories or other big structures. Process twins help to observe complicated interactions and allow synchronization of elements to achieve the highest possible efficiency of operation.

What is the difference between digital twins and simulation?

The concepts of simulations and digital twins are sometimes used in reference to the same digital objects, but they are different things and the underlying concepts are different. Simulation is usually created in a platform such as CAD software or other and it doesn’t have to have any relation with any real physical object.

A digital twin, on the contrary, has to be created in connection with the real physical object. The whole process of the creation of digital twins is based on real data related to the object. Digital replica is linked with the real object through IoT sensors and gets real-time data, which means that data is constantly updated. The flow of information between the digital twin and the object is two-way. The results of processes that occur in digital twins may be used back with the real, original object.

Main areas of application of digital twins

The creation of digital twins is still very expensive and laborious and currently, the usage of digital twins is limited to certain areas. They are used extensively in the areas such as:

• Urban planning

Digital twins of the towns and cities can show realistic spatial data in 3D and 4D systems. The application of twins allows for more consistent urban planning of the environment. Currently, more and more cities decides to join Shanghai and Singapore and create their twins. All data is available in real-time so it improves greatly all the operations related to the design of streets, buildings, and transport systems. Most of the new cities that are being built recently in the Middle East have their digital twins created at the same time.

• Automotive industry

The second very important application of digital twins we may find in the automotive industry. Cars have complicated structure that involves many systems that have to work together without the risk of breakage. Digital twins help to increase the performance and efficiency of vehicles. In the case of cars that run with the use of the support of AI digital twins allow an early check of the validity of prognostics.

• Healthcare

Initially, all the digital twins used in the healthcare industry were replicas of products and equipment. Currently, the digital twin concept in healthcare is being applied to patients that need to be diagnosed and monitored. Fully personalized models of patients are being built. Those replicas allow storage and tracking of lifestyle parameters. Digital twins of the whole population will enable greater pattern search.

• Manufacturing industry

Digital twins show the functioning of the object through its whole life cycle. The very first application of digital twin technology was in manufacturing. All complicated products can be produced with the use of digital twins and those may be utilized at every stage of the production.

• Power plants

Digital twins help to set the schedule for such huge, complex entities as power plants. All sorts of large engines and turbines have to be maintained regularly and their operation depends strongly on cyclical processes that have to be managed orderly to keep them in operation. Digital twins seem to be indispensable here.

Advantages of digital twins for the industry

• Risk reduction

The health and safety of employees as well as of the environment can be protected by the usage of digital twins software. Business objectives can be kept through the reduction of accidents during the production process. With digital twin technology, it is much easier to avoid costly downtime.

• Quality of products

With the better insight provided by simulations run through digital twin technology companies can improve the quality of their products and customize them in real-time, when the necessity occurs.

• The cost of maintenance is lower

Digital twins provide the company with predictions related to the whole process of production. This allows them to schedule repairs or changes of parts before anything breaks down.

Future applications of digital twins

There is no limit of the future application of digital twins. Currently, digital twins are still learning their new skills, but the amount of money invested and the amount of cognitive power increase each year exponentially. Nowadays the market for digital twins is growing mainly in several leading sectors such as manufacturing, automotive, aviation, energy, healthcare, and logistics, but it is predicted that its future development will cover all possible areas of human activity.

• Smart cities

The main reason for the scientists that wish to create digital twins for cities and towns, is the carbon emission that is generated by huge urban structures. It has been estimated that cities produce approximately 70% of carbon and consume more than two-thirds of the world’s energy. It is expected that many cities such as Miami, New York, San Diego, and Jakarta, along with the significant area of land in China and India will be underwater by 2050.

More sustainable planning of urban development and better control over time and resources used by the construction industry (currently highly inefficient) may help reduce the waste of energy and emission of carbon to the atmosphere. Digital twins of cities are the solution to the problem – they can help optimize all processes of urbanization such as design, planning, construction, and day-to-day management. Digital twins can reduce the wastage of energy coming from poorly insulated, old buildings, as well as lower the emission coming from the ineffective traffic flow.

Currently, there are many urban city projects all over the world in progress. Those cities don’t have their digital twins yet, but it is there to come if governments and local states expect to maintain them flawlessly. The scale of the project is immense – most the huge cities aim to be smart sooner or later. Ministry of Housing and Urban Affairs of the Government of India has selected 100 cities to become smart by 2023. The states and the government are the major investors and initiators of the project of Indian smart cities. All smart Indian cities are expected to allow the participation of smart people through the use of ICT (mobile-based tools).

China has a higher number of smart city projects in operation – almost 500. There are 100 leading cities, with the most known on the list such as Beijing, Shanghai, Shenzhen, Hangzhou, Chengdu, and Wuhan. Most of them are still under construction, but it is expected that China will be the very first of the countries on Earth to be fully smart in the future.

Some of the biggest smart cities are located in the United States, and those are New York, Los Angeles, San Francisco, Phoenix, Salt Lake City, Denver, Miami, and Minneapolis. London and Dubai are becoming smart as well, similar to Paris, Jakarta, Melbourne, Barcelona, and Taipei. As we see most of the biggest cities aim to become smart in the nearest future, and all of them will have their digital twins eventually.

• The Earth and the agriculture 

For further control over the emission of carbon dioxide, the twin of Earth is being developed. It is stated that the digital twin should help to predict changes in climate and predict the outcomes of such changes. The project started in mid-2021 and it is expected that it will run for ten years at least. In the end, the full, highly accurate digital model of the Earth will be designed. The replica of Earth aims to represent all processes that occur on the Earth’s surface. Food, water, and energy will be monitored through this twin as well.

Some of the solutions related to digital twins are used in agriculture already to boost the production of crops such as corn, wheat, and soy. The sensors monitor the weather and the quality of the soil. Natural events such as tornados, floods, and droughts pose a risk to food security, accurate predictions provided by digital twin technology may be the solution for agriculture.

• Human digital twins

A digital world of smart digital cities may need digital human inhabitants to thrive. Humans can have their digital twins and the technology already allows the creation of such entities. Digital twins of humans that will lead digital life are expected to become in use before the end of the decade, which is pretty soon. Those digital twins are predicted to be thinking and learning from real models – humans.

Threats and concerns

Digital twins of humans can be useful for employers, businesses, and companies, but would it be equally beneficial for individual people? It may prove highly unethical and detrimental to human interactions. Human digital twins that will learn the needed skill may replace humans and make them redundant. Employers may decide to employ a digital human instead of a real person, which is only understandable comparing the costs of living of both creatures.

The end of freedom and free will. Among the praises over the functionalities of cities’ digital twins are those that may not be as beneficial for humans at all. It is being said that governments, or other managing entities, may be able to control all vital aspects of living in urban space. The digital twin will be able to control traffic flow – meaning that one may not be able to travel where he wishes and to control the temperature of rooms and water used in citizens’ flats – meaning that you may not be able to have long showers anymore.

The amount of power and control granted by the technology of digital twins to unknown, unpredicted authorities that will govern our cities is excessive and dangerous – there will be a million ways to punish disobedient individuals, just by the manipulation of the immediate environment of the person (lack of access to services, restricted amount to resources, limited ability to travel).

Real humans may be forced to live in small, confined places, with no access to nature or other living creatures. As retail companies plan to move into virtual reality, shopping will be realized throughout virtual mediums as well. It seems that some of the major designers of digital twins technology have forgotten that shopping is not only about acquiring goods but has more to do with: socializing, entertainment, and refreshment for the mind.

Currently, there are living examples of smart cities that can be analyzed in relation to ethics and freedom issues. Beijing is being praised for its payment system that allows cashless existence across the whole city – would all citizens of the world be forced to have a smart device? Guangzhou has replaced ID cards with smartphones – the system is fully operational, but allows a greater amount of control over the individual citizens than was ever possible. People criticizing the government don’t have a chance to hide in Guangzhou anymore.

The most crucial question about digital twins of the real space (cities, Earth, objects) and digital twins of humans is about the link between the human and his replica that is required for the whole process – would it be a smartphone or other device. The change into digital reality seems to be necessary for some areas of human life, but the technology of digital twins is very energy-consuming. Should we not ask ourselves if all the applications of the technology of digital twins are really necessary in all aspects of our living and who exactly wishes to benefit from having making human prototypes live with thinking twins in some unreal, unnatural, and fully-controllable world?

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ISTQB testing principles – table of contents:

1. Testing reveals defects but cannot prove their absence
2. Thorough testing is impossible
3. Early testing saves time and money
4. Malfunction snowball effect
5. Pesticide paradox
6. It depends on the context
7. Advertising flawless software is a no-go
8. Summary

Testing reveals defects but cannot prove their absence

Testing increases the probability of finding mistakes, which in turn facilitates chances to fix them. However, it cannot fully guarantee that software is free of all defects even if the vast majority get spotted and fixed. Due to the inability to create flawless software, many consider the process as negative by design, as you’ll never get a positive result and always find some “dirt” in the programs.

Thorough testing is impossible

The above rule of thumb states that detecting all malfunctions of software is futile. However, that doesn’t apply to simple short programs. This, in turn, indicates that there is a chance to see all combinations of inputs and preconditions to test some programs completely. When evaluating sophisticated software, even the best AI can’t execute all necessary measurements, let alone manual testers. Automated assessors will run through apps more efficiently and accurately, but they still cannot guarantee flawless performance. To do so, you have to embark on additional tasks like prioritizing, risk analysis, as well as finding and running other testing techniques.

Early testing saves time and money

Many professionals also call this principle “shifting left.” The sooner you spot defects, the easier you can fix them, hence static and dynamic testing should begin as soon as possible. In a nutshell:

• Static testing – assesing tne product without running the code.
• Dynamic testing – evaluation of the code of a module or system during its performance

Detecting defects in the first phases of implementation facilitates further diagnosis. But when two areas of software interact, amending defects becomes troublesome due to the inability to pinpoint the one that has the error. In such cases, it takes extra time, effort and manpower to tackle. All in all, it’s the rapid response to surfacing obstacles that can prevent cracks from multiplying

Malfunction snowball effect

Most glitches tend to cluster in most critical modules, so their in-depth examination reveals and sufficiently eliminates most. These groups become the major focus of running risk analysis to map out and establish the future conduct of actions. The majority of flaws surface after following the paths the users take but in these cases, knowledge alone doesn’t render the modules are impeccable.

The Pareto principle, says that 80% of results originate from only 20% of causes. In other words, 80% of bugs exist in 20% of modules. If you encounter numerous malfunctions in a module, keep digging as they’ll be there.

Pesticide paradox

Running the same tests repeatedly may fail because they may have been designed incorrectly in the first place and will never prove effective. You have to amend and upgrade testing to increase the chance of finding new faults in the software.

Creating a completely new system of diagnosis won’t do the trick either. Following the previous combinations may stop the assessment process at the same level. This principle is coined ‘pesticide paradox’ because pesticides that control pests also lose effectiveness after a given amount of use.

It depends on the context

The way of executing testing depends on the subjects examined. Thus, testing an accounting program, a video game, or a social networking application vary substantially. It also depends on the situation, for instance, an analysis focusing on the practicality of an app like checking its attractiveness to users, ease of use, visual layer, etc. also differs from those evaluations aimed at functional attributes of the program, e.g. performing correct calculations.

Advertising flawless software is a no-go

Applying various types of diagnostic tools cannot guarantee spot-on apps. Many who claim and advertise their apps as such are wrong, yet probably it is only for the marketing efforts they make the claim. You can execute multiple manual and automated tests to increase the probability of uncovering and fixing as many errors as possible, but still, there is no guarantee of perfect performance. In some cases, the obstacles concern operating software, e.g. the program may not meet all user expectations.

ISTQB testing principles – summary

This is how ISTQB, at a basic level, presents seven ISTQB testing principles that a software tester should follow. First, they indicate the infeasibility of full software diagnosis, hence it is crucial, among other things, to modify tests, as well as to conduct a thorough search in the key modules. These actions enhance the search and clearance of the majority of defects decreasing the likelihood of failures in the future.

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Predictive analytics software – table of content:

1. What is predictive analytics?
2. Predictive analytics software
3. Mathematica
4. Sisence
5. Minitab
6. IBM SPSS
7. Alteryx Designer
8. SAP Analytics Cloud
9. SAS Visual Analytics
10. Predictive analytics tools – how it works?

What is predictive analytics?

Predictive analytics is a type of research that allows you to forecast future events and generate deep insights into the processes. There are many benefits of predictive analytics since this method gives particularly accurate readings that can be used to resolve various problems. Among many issues that can be addressed with the predictive analytics are: fraud detection, risk reduction, marketing campaign optimizing, decision making improving, and operations efficiency improving.

The main areas or industries that use predictive analytics include sectors of: banking and financial, retail, healthcare, pharmaceuticals, insurance, utilities, public and governmental, and aerospace. All needed insights have to based on data, hence predictive analytics software is needed to process, calculate and organize all the initial information.

Only the best solutions allow quick and accurate analysis of datasets. In this article, we have listed predictive analysis software available on the market that should meet you needs regarding you budget and operational requirements.

Predictive analytics software

Mathematica

Mathematica is a software developed by Wolfram Research that deals with mathematical problems with a single click. It allows data visualization and presentation both in 2D and 3D. It is user friendly, versatile. IT can be integrated with C++, Python and other languages.

The main features of the software include: ad hoc reporting, convolutional neural networks, customizable dashboard, data management & visualization, document classification, image analysis, model training, neural network modeling, reporting, analytics, self-learning, visualization.

Type of software:reporting, statistical analysis, business intelligence.

Sisence

Sisence is platform that offers its services all over the world. The platform is mainly used by managers and data scientists. It offers good quality visualization features and interpretative tools.

The main features of the software include: dashboard, ad hoc query, reporting, machine learning, API, AI, benchmarking, budgeting, forecasting, chat-bot, collaboration tools, conversion tracking, customizable branding.

Type of software:data analysis software, data visualization software, integration software, HR analytics software.

Minitab

Currently Minitab software is used by thousands of business worldwide. The analysis tools of the application are easy to use for all clients regardless their scientific background, therefore not only statistics graduates can use the program. Minitab provides statistical, predictive and improvement analysis in one solution.

The main features of the software include: discovery of association, tools for collaboration, dashboard, data analysis, data blending, data capture, data transfer, data discovery, data import, data export, data visualization, forecasting tools.

Type of software:data analysis software, statistical analysis software, reporting software.

IBM SPSS

SPSS made by IBM is a well-known, popular product that came out first in 1968. SPSS is a specialized tool for advanced statistical research. Recently it is available as two products: SPPS Statistics and SPSS Modeler, where the first is typical statistical software, while the second one transforms the data into visual representations. SPPS is great for large businesses that deal with statistical analysis and data modeling as well as for academics and data scientists.

The main features of the software include: instant reporting, budgeting, collaboration aid, dashboard, data discovery and visualization, KPI, machine learning, predictive analysis, statistical analysis.

Type of software:statistical analysis, reporting.

Alteryx Designer

Alteryx used to be known under a name SRC LLC but the company rebranded into its current name in 2010. The APA platform produced by Alteryx is one of a kind platform for non-coders. It contains building blocks that can be applied to your data. The benefit of the platform is that it can easily handle unstructured data.

The main features of the software include: reporting, discovery of associations, collaboration aid, database of customers, dashboard, data analysis tools, data: blending, cleansing, discovery, extraction, integration and mapping.

Type of software:statistical analysis, business intelligence integration.

SAP Analytics Cloud

Probably one of the best predictive analytics applications currently on the market. It is produced by German company, which has started long ago in the 70s. The software is great for businesses of any size since it is easy to use for non-technicals. It is good both for data mining, augmented analytics and predictive analytics.

The main features of the software include: dashboard, queries, reporting, API, benchmarking, collaboration, reports, templates, data blending, data export, data import and discovery.

Type of software:predictive analytics, data visualization, business intelligence.

SAS Visual Analytics

SAS is best for large businesses. The modules of the platform deal easily with advanced text analysis, statistical analysis and forecasting. The software is able to deal both with structured and unstructured data.

The main features of the software include: dashboard, reporting, machine learning, AI, API, discovery of associations, collaboration aid, reports, data management and transformation.

Type of software:business intelligence, data analysis, predicative analytics, reporting and statistical analysis.

Predictive analytics tools – how it works?

The tools presented above offer no-code solutions, data lakes and machine learning algorithms. Some of them are complete, expanded, multi-functional workspaces, while other offer just basic functions. To select the best predictive analytics software for your company you have to know your own needs well.

Predictive analysis uses historical data to search for patterns, schemes and repetitive occurrences of events. To facilitate predictive process machine learning algorithms as well as regression analysis are used. It seems that without good quality software for predictive analytics realistic decision making related to the marketplace is not longer possible.

Read also: 10 best content creation tools

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What is software testing? – table of content:

1. What is software testing?
2. Software testing objectives
3. What does the job of a software tester look like?
4. How to become a tester?

What is software testing?

Software developed by programmers is subjected to tests that consist of checking its correct operation and compliance with the specification. This is called software verification.

Specification is a document that has all the necessary information about the software, e.g., an application, and first it contains a description of the functions it should perform. Checking if the software works according to the users’ expectations is called validation.

Testing improves software quality, limits the occurrence of failures resulting from errors, reduces financial losses, shortens the time of software development and strengthens the company’s image. Testing activities are best performed in the earliest stages of software implementation. The sooner a bug is detected, the lower the cost of fixing it. If tests are not carried out in the earliest stages of software development, complications may arise due to e.g., advanced work on the project, which may make it difficult to locate the source of the error and fix it.

Software testing objectives

According to ISTQB (International Software Testing Qualifications Board), testing objectives can be achieved by:

• Prevent defects resulting from errors by evaluating work products such as code,
• Reduce the risk of software quality deterioration by detecting failures caused by defects,
• Verification that the software meets all the requirements that were included in its documentation, validation, that is, verifying that the software aligns with user expectations,
• Building confidence in testing at every level,
• Providing information that influences stakeholders to make informed decisions,
• Compliance with laws, standards, norms and contractual requirements.

What does the job of a software tester look like?

A tester’s job is to report bugs found as a result of software exploitation. In case of finding them, he reports them to a programmer responsible for a given area or the whole software. Dedicated programs are designed for this type of reporting. The tester describes the path in which he presents step by step the way the error was detected. This allows the programmer to quickly find the bug and fix it, and then report the completed task to the tester for retesting. The tester, in turn, retraces the path by repeating the same sequence of steps. If the bug is fixed, he accepts the task, and if it is repeated, he reports it again to the programmer.

Testers are divided into manual and automated testers. As a manual tester’s job is to single-handedly test the software by assessing each test scenario containing all cases, is time-consuming. An automated tester, in turn, uses tools to write scripts and programs that speed up the the process and additionally detect errors that may go unnoticed by a manual tester. Automatic testing is, therefore, more precise, but creating tools requires knowledge of programming languages, e.g., Java, SQL or Python.

How to become a tester?

Anyone who plans to become a software tester should learn the theory of testing. The knowledge gained can then be verified by taking the ISTQB exam. This is a non-profit organization that has been operating since 2002. The exam has the form of a test, and after obtaining a positive result, the examinee receives a certificate confirming his or her competence and knowledge as a tester. The advantage of the exam is that there is no expiration date for the certification and it is recognized worldwide.

The ISTQB exam can be taken at three levels: Core, Agile, and Specialized. Core addresses the multi-faceted topic of software testing. Agile refers to agile software testing practices, and the specialist module, for example, provides information related to specific testing activities such as test automation. There are also three levels of exam difficulty: basic, advanced, and expert. Below is a diagram of the mentioned modules and their levels.

A tester plays a vital role in the proper functioning of the software, so he or she should possess qualities that will make the job easier. Accurate, patient, consistent and communicative individuals will do best in software testing. A tester should also exhibit analytical thinking skills due to the implementation of test scenarios.

Summary

Software testing is a way of detecting irregularities in software and improving its quality by meeting testing objectives. The entire process is handled by software testers who have the specific knowledge to facilitate their work and they can confirm it by e.g., obtaining the ISTQB certificate.

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Software errors – table of content:

1. Reasons for software errors
2. Life cycle of an error
3. Significance levels of errors in software
4. Summary

Reasons for software errors

The errors in software come are caused by so called bugs or failures and those are main reasons for bad functioning of the software. The occurrence of errors is possible at any stage of creation of the software. Most often errors are to be found in the code itself and are caused by human actions. The defect found in one part of the software may have an impact on the other parts of the same software, especially if both are connected. An error is a result of a defect, both may lead to serious failures, which can manifest themselves during running of the software.

Accordingly to International Software Testing Qualification Board, there are many different reasons for software errors and those are:

• human imperfection
• pressure of time
• lack of qualification, skill or experience
• bad communication in the team
• invalidly written specification
• complexity of: code, project or technology
• misunderstandings related to the interface inside the system and between systems – especially in case of a high number of those systems
• implementation of new technologies

As it was mentioned before, errors lead to defects and defects lead to failures, here it is wort to note that failures not always are caused by a human error. The occurrence of failures may be caused by other phenomena such as: radiation, electromagnetic field, or contamination. Such factors influence the operation of the software by changing the external conditions that affect the hardware.

Life cycle of an error

Life cycle of an error is a process, which begins at the moment of error’s discovery and reporting to the moment of the fixing and task closure. The length of the cycle depends from the severity of the error Complexity of the error may significantly extend or shorten the time needed for fixing. There are many programs that are used to monitor software errors, and the choice of the program may affect the life cycle of an error.

The exemplary life cycle is simple. At the first stage the error is reported in the notification and gets an open status. In the notification there is a path that allowed the tester to find the error. Additionally, the programmer, who deals with the task is assigned in the notification and as soon as he starts to fix the error its status changes into in progress. This information is particularly important for the tester, who can start testing the software as soon as the notification changes into ready for test.

The second component is the feedback. When all the surveys are being filled up by the respondents one nice and tidy report has to be created. The whole information has to be reviewed attentively so the all important insight can be gathered. This is particularly important because it is easy to misinterpret the findings. Survey used for the given case has to well adjusted and sound.

The tester starting his operation changes the status into test in progress and after finishing chooses one of two options: resolved (in case of successful repair) or reopened (if software error still needs reparation). In such way programmer get notification about the necessity to restart work on the error.

Significance levels of errors in software

The software errors may be divided accordingly to significance levels, which is being granted by testers. The significance level pass the information about the impact of the error on program functioning. The higher significance level of an error the faster it should be fixed.

The highest of level of significance is called blocker since its occurrence means the complete lack of further ability to use the program and necessity to stop testing it. The example of the blocker is a software error, which turns off the application. Such error requires quick fixing to enable further testing.

The lower level of significance is called critical because the software error of that level makes using all the functions of the program difficult. Such software error doesn’t block completely testing, but it has great impact on testing process, and the software cannot be released without fixing this problem.

The major level of significance is quite important but it doesn’t hold the testing process.

The error of medium level of significance is an error of moderate gravity, that does require fast reaction since it does not obstruct tests making.

5. Empowerment of individuals – when 360 degree feedback is used all employees have a chance to voice their concerns. Individuals may feel that they have a voice and that the company respects their view and opinion, hence any type of action in result employees are heard has a positive impact on staff morale.

The lowest level of significance of error, namely trivial, means that the software error is of small importance.

Summary

The errors in the software have many causes but they occur mainly due to the unconscious human action. It is necessary to check the accuracy of any software by continuous testing. Each software error has a cycle of living, which ranges from its discovery to fixing. Errors have different levels of significance, each has the same importance, but some block the possibility of using the software to the extent that it cannot even be tested, while other don’t influence those processes as much and are easy to fix.

Read also: 4 types of employees every company needs

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Python applications in practice – creating a guessing numbers game

This mini-project will be exciting to learn on how we can use functions and most of the other things which we learned in the previous blogs. This mini-project game generates a random number from 1 to 1000 or if you want it to be easy you can decrease the range and the user who is playing the game must guess the number. Sounds exciting, doesn’t it? What will make it more exciting is that we can give the user some cues if he guesses the number wrong so that they can guess the number correctly.

Let’s make a blueprint for the game with Python applications in practice.

Intro command line

In the intro command line, we will ask the user to guess a number. We will ask his name and age. Then we will ask him if he wants to play the game or not. Let’s do this in the code.

# Intro Panel Command line
 
print("Welcome to the guessnum")
 
name=input("what is your name?")
print(f"Hello {name}")

Output:
Welcome to the guessnum
Hello john

As can be seen we first introduced our game to the user and then we asked the user their name. we greeted them using the saved name. Now let’s ask the user the age.

# Intro Panel Command line
 
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
print(f"Hello {name}")

Output:
Welcome to the guessnum
Hello john

In here we are seeing fstring, this is alternative to format, if we write f followed by a string, we can use our stored variables inside the “{}” directly.

Now we can see most of the intro panel. Now let’s ask the user if he wants to play the game and if he wants to play the game, lets ask him to guess a number and we can say if its right or not. But before asking the user to guess the number, we must have the number of the program ready. Let’s see how it is done in code.

# Intro Panel Command line
 
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
choice=input(f"Hello {name}, would you like to play the game? y/n")
 
if choice=="y":
    pass
else:
    print("exiting")
    exit
 

Now we are making another prompt which will ask the user, whether he wants to play the game, and we will be using the conditionals which we learned in the previous blogs to continue if he says yes and if it’s no, to exit the game. Now let’s continue expanding our game and ask the user for the number, but before that let’s make our code select a random number.

# Intro Panel Command line
import random
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
choice=input(f"Hello {name}, would you like to play the game? y/n")
 
if choice=="y":
    number=int(random.randint(1,5))
    guess=int(input("Please input your guess"))
    print(f"your guess is {guess}")
else:
    print("exiting")
    exit
 
 

Output:
Welcome to the guessnum
your guess is 2

Now we added an import known as random which selects a random number from a given range. The function is random.randint(start,end). Then we are asking our user to guess the number and we are printing our users guess.

Let’s also print our program’s guess.

# Intro Panel Command line
import random
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
choice=input(f"Hello {name}, would you like to play the game? y/n")
 
if choice=="y":
    number=int(random.randint(1,5))
    guess=int(input("Please input your guess"))
    print(f"your guess is {guess} and program's guess is {number}")
else:
    print("exiting")
    exit
 
 

output:
Welcome to the guessnum
your guess is 2 and the program's guess is 5

So, we can see that we are almost halfway, we have the guess of the program and the guess of the user. Now we can just compare and print if the user is correct or not.

# Intro Panel Command line
import random
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
choice=input(f"Hello {name}, would you like to play the game? y/n")
 
if choice=="y":
    number=int(random.randint(1,5))
    guess=int(input("Please input your guess"))
 
    if guess==number:
        print("you guessed it right!!!")
 
   
 
    print(f"your guess is {guess} and program's guess is {number}. Sorry!!! your guess is wrong")
 
else:
    print("exiting")
    exit
 
 

output:
Welcome to the guessnum
your guess is 2 and the program's guess is 1. Sorry!!! your guess is wrong

As you can see, I have guessed wrong maybe you can guess it right. This game can be made more interesting by adding the score factor. Now let’s code for the score factor.

# Intro Panel Command line
import random
print("Welcome to the guessnum")
 
name=input("what is your name?")
age=int(input(f"Hello {name}, what is your age?"))
choice=input(f"Hello {name}, would you like to play the game? y/n")
correct=0
 
 
   
 
while(choice=="y"):
    number=int(random.randint(1,5))
    guess=int(input("Please input your guess"))
 
    if guess==number:
        print("you guessed it right!!!")
        correct+=1
        choice=input(f"Hello {name}, would you like to continue the game? y/n")
           
 
   
   
 
    print(f"your guess is {guess} and program's guess is {number}. Sorry!!! your guess is wrong")
    choice=input(f"Hello {name}, would you like to continue the game? y/n")
       
 
 
else:
    print(f"your score is {correct}")
    print("exiting")
    exit
 
 

output:
Welcome to the guessnum
your guess is 1 and program's guess is 5.
Sorry!!! your guess is wrong your guess is 2 and program's guess is 3.
Sorry!!! your guess is wrong your guess is 3 and program's guess is 2.
Sorry!!! your guess is wrong your guess is 4 and program's guess is 3.
Sorry!!! your guess is wrong your guess is 1 and program's guess is 2.
Sorry!!! your guess is wrong your guess is 2 and program's guess is 5.
Sorry!!! your guess is wrong your guess is 3 and program's guess is 4.
Sorry!!! your guess is wrong your guess is 3 and program's guess is 2.
Sorry!!! your guess is wrong your guess is 3 and program's guess is 5.
Sorry!!! your guess is wrong your guess is 4 and program's guess is 2.
Sorry!!! your guess is wrong your guess is 3 and program's guess is 1.
Sorry!!! your guess is wrong your guess is 4 and program's guess is 5.
Sorry!!! your guess is wrong your guess is 2 and program's guess is 2.
you guessed it right!!!
Sorry!!! your guess is wrong your score is 1 exiting

As you can see, we utilized while loops and we used a new variable called correct, which is giving us the score of the user. Which we are printing to the output.

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Python files – table of contents:

1. Files in Python – definition:
2. Examples of binary files in Python
3. Examples of text files in Python
4. Operations on files in Python
5. Functions involved in reading files in Python

Files in Python – definition:

A file is an entity that stores information. This information may be of any type such as text, images, videos, or any music. In python, there are functions inbuilt which can be used to perform operations on files.

Examples of binary files in Python:

1. Document files: .pdf, .doc, .xls etc.
2. Image files: .png, .jpg, .gif, .bmp etc.
3. Video files: .mp4, .3gp, .mkv, .avi etc.
4. Audio files: .mp3, .wav, .mka, .aac etc.
5. Database files: .mdb, .accde, .frm, .sqlite etc.
6. Archive files: .zip, .rar, .iso, .7z etc.
7. Executable files: .exe, .dll, .class etc.

Examples of text files in Python:

1. Web standards: html, XML, CSS, JSON etc.
2. Source code: c, app, js, py, java etc.
3. Documents: txt, tex, RTF etc.
4. Tabular data: csv, tsv etc.
5. Configuration: ini, cfg, reg etc.

Operations on files in Python

Opening a file in Python:

The open() function in python is used for opening files. This function takes two arguments, one is the filename and the other one is the mode of opening. There are many modes of opening such as read mode, write mode, and others.

Let’s explore the syntax:

# File opening in python
 
File=open(“filename”,”mode”)
 

“r”:– this is used for opening a file in read mode.

“w”: – this is used for opening a file in write mode.

“x”: – this is used for exclusive file creation. If the file is not present, it fails.

“a”: – this is used when you want to append a file without truncating the file. If the file is not present, then this creates a new file.

“t”: – this is used for opening file in text mode.

“b”: – this is used for opening file in binary mode.

“+”: – this is used when the user wants to update a file.

Note:

Let’s open a file using above discussed methods. The code is illustrated below. As we don’t have any file, we will create a file and then open it.

x="new file opening"
 
with open("new","w") as f:
    f.write(x)

In the above code, we are creating a string variable x which contains the text “new file opening”, this string variable is being written into a file “new” using write method. We are using “with” here as it handles closing of the file. So, we are opening a file in write format and writing the string x to the file.

Now, let’s read the same file.

x="new file opening \n writing new file"
 
with open("new","r") as f:
    print(f.read())

In the above code, we are opening the file new which we wrote in the previous code and opening it in read format. Note that, we are using read() function to read the file. Let’s run and see the output.

#output
 
New file is opening
 

Functions involved in reading files in

There are three functions involved in the reading operation performed on files.

This function is used when the user wants to read all the information inside the file.

x="new file opening \n writing new file"
 
with open("new","r") as f:
    print(f.read())

This function is used when the user wants to read the file line by line.

x="new file opening \n writing new file"
 
with open("new","r") as f:
    print(f.readline())

This function reads all the lines but in a line by line fashion which increases its efficiency in handling memory.

x="new file opening \n writing new file"
 
with open("new","r") as f:
    print(f.readlines())

Appending a file:

As discussed above, we will be opening a file in append mode which “a+” for appending it. The code is illustrated below.

x="new file opening"
 
with open("new","a+") as f:
   
    f.write("Hello world")

Reading the file to see the appended line:
x="new file opening"
 
with open("new","r") as f:
   
    print(f.read())
 

Let’s explore the output:

new file openingHello world

Renaming a file:

For renaming a file, we will be using the methods present in the “os” module of python. The code is illustrated below.

import os
 
os.rename("new.txt","example.txt")

In the above code, we are importing the “os” module and using “rename” method to rename the file we create from “new” to “example”.

Removing a file:

For removing files, we will be using the same module “os” which we have used for renaming the file. The example of the code is illustrated below.

import os
 
os.remove("example.txt")

Copying a file:

For copying the file, we will be using the same module “os” which we have used for renaming and removing a file. The example of the code is illustrated below.

import os
 
os.system("cp example example1")

Moving a file:

For moving the file, we will be using the same module “os” which we have used above. The example of the code is illustrated below.

import os
 
os.system("mv source destination")

In this blog, we have covered some basics when it comes to files in Python. In the next blog post we will use all the gathered knowledge in practice.

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Python classes and objects – table of contents:

1. Python classes
2. Python classes – definition
3. Initialization of Python classes
4. Let’s write our first Python class
5. Attributes
6. Behavior of the class
7. Objects in Python
8. Inheritance

Python classes

As we have discussed in the first blog, Python is an object-oriented programming language. There are three phrases that are very important while discussing object-oriented programming in Python. The first one is class, the second one would is an object, the third one would be the inheritance. Let’s start with what is a class.

Python classes – definition

A class is a blueprint or an extensible program that helps in the creation of objects. A class consists of behavior and state. The behavior of a class is demonstrated through functions inside the class which are called methods. The state of the class is demonstrated using the variables inside the class which are called attributes.

Initialization of Python classes

A class can be initialized using the following syntax.

A class in python is defined using “class” keyword following the class name. The basic syntax of Python function is illustrated below.

For Example:

<img src="/wp-content/uploads/Python_9-800x600.png" alt="Python_classes" width="800" height="600" class="alignnone size-medium wp-image-21409 img-fluid" />
# Create a function
# class Monkey
class classname:

Note: class name is also having the same norms as the variable declaration.

Let’s write our first Python class

# first class
 
class Animals:
	pass
 

In the above code block, we have written a class which we will be developing further in the blog. As you can see, we have used “class” keyword.

Let’s now see, how to add components to the animal’s class. But before that let’s learn about the “__init__()” constructor. Constructors are used for object instantiation. Here the __init__() is used for object instantiation. The constructor can be default with only self as an argument or parametrized with required arguments.

Attributes

There are two different types of attributes, the first ones are class variables and the second ones are instance variables. The class variables are the variables that are owned by the class. Also, these variables are available to all instances of the class. Hence, their value will not change even if the instance changes.

# class variables
 
class Animals:
	type=”mammals”

The instance variables are the variables that belong to the instances itself. Hence, they will change their value as the instance changes.

# class variables
 
class Animals:
	def __init__(self,legs):
		self.legs=legs
		

Note:Instance variables are not available for access using class name, because they change depending on the object accessing it.

Let’s make a program that has both class and instance variables declared.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
 
	

In the above program, we have used both instance and class variables. So, these variables form attributes of the class.

Behavior of the class

As discussed, behavior of the class is defined by the methods inside the class. But before going into the discussion on behavior, we have to start discussing the “self” parameter, which we used in the __init__().

Self:

In a very simple term, whenever we attach anything to self it says that the variable or function belongs to that class. Also, with “self”, the attributes or methods of the class can access.

Methods:

Class methods are functions inside the class which will have their first argument as “self”. A method inside the class is defined using “def” keyword.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 

In the above method “bark”, as we are using the name variable, which is an instance variable, we are accessing it using “self” and this function would print “woof woof!!!”, only if the name provided to the object, is dog.

We have discussed most of the components of a class, but you might be thinking how to see if the class is working. The answer to this is unless we create an object of the class, we will not be able to see what the class is doing. Now, Let’s define and create an object of the class.

Objects in Python

An object is an instance of the class. A class is just a blueprint, but the object is an instance of the class which has actual values.

The code for defining or creating an object is illustrated below.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 
dog=Animals(“dog”,4)
 

To create an object, the syntax is the objectname=classname(arguments). Hence, here we are giving the name of the animal to be dog and number of legs to be 4. Now, the object of the class is created, the next step is to use the object to access its attributes. To access the attributes of a class using the object, remember only the instance variables can be accessed using the object. The instance variables in our class are name and legs.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 
dog=Animals(“dog”,4)
print(dog.name)
print(dog.legs)
 

As we can see, we are able to access instance variables using dot notation.

Let’s explore the output.

#Output
 
dog
4
 

To access the functions inside the class or methods, we will be using the dot notation. The example is illustrated below.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 
dog=Animals(“dog”,4)
print(dog.name)
print(dog.legs)
print(dog.bark())

#Output
 
dog
4
woof woof!!!

In the above example, we can see that we are accessing the class method “bark” using the dog object we created. We can see that we are not using the “self” parameter in the function arguments. This is because we don’t require the use of “self” outside the class as the object itself is acting as self.

Inheritance

Inheritance is a process through which the class attributes and methods can be passed to a child class. The class from where the child class is inheriting is the parent class. The syntax for inheritance is illustrated below.

#Inheritance
 
class parent:
 
class child(parent):
 

From the above illustration, we can see that for the inheritance syntax we are placing the parent class name as an argument to the child class. Let’s use the Animals class and make a child class called dog. This is illustrated below.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 
class Dog(Animals):
def __init__(self,name,legs,breed):
Animals.__init__(self,name,legs)
self.breed=breed

In the above example code, we made a dog class which is extending the animals class which we created before. We are also using the parameters from the Animals using the Animals.__init__(arguments) which has name and legs which will be inherited to the dog class. Then we are making an instance attribute for the dog class which is breed.

Now let’s make an object for the dog class and access the attributes and methods of the animals class.

class Animals:
	type=”mammals”
	def __init__(self,name,legs):
		self.name=name
		self.legs=legs
	def bark(self):
		if self.name==”dog”:
			print(“woof woof!!!”)
		else:
			print(“not a dog”)
 
class Dog(Animals):
def __init__(self,name,legs,breed):
Animals.__init__(self,name,legs)
self.breed=breed
 
 
pug=Dog("dog",4,"pug")
pug.breed
pug.name
pug.legs
pug.bark()

#Output
 
pug
dog
4
woof woof!!!

As we can see from the output the parent class attributes and methods are being accessed by the child class object.

In this blog, we have covered some basics of classes in . In the next blog post we will cover the topic of file handling.

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