Previous API Keys and their security. Next Software Testing Accessibility Testing. Recommended Articles. Article Contributed By :. Easy Normal Medium Hard Expert. Writing code in comment? Please use ide. Load Comments. What's New. And also, ensure that the module we are adding is the parent of the earlier ones. Whenever the data flow is complex and very difficult to classify a parent and a child, we will go for the non-incremental integration approach.
The non-incremental method is also known as the Big Bang method. Whenever we are done with the unit and integration testing, we can proceed with the system testing. In system testing, the test environment is parallel to the production environment. It is also known as end-to-end testing. In this type of testing, we will undergo each attribute of the software and test if the end feature works according to the business requirement.
And analysis the software product as a complete system. The next part of black-box testing is non-functional testing. It provides detailed information on software product performance and used technologies. Non-functional testing will help us minimize the risk of production and related costs of the software.
Non-functional testing is a combination of performance, load, stress, usability and, compatibility testing. Non-functional testing categorized into different parts of testing, which we are going to discuss further:. In performance testing, the test engineer will test the working of an application by applying some load.
In this type of non-functional testing, the test engineer will only focus on several aspects, such as Response time, Load, scalability, and Stability of the software or an application. While executing the performance testing, we will apply some load on the particular application to check the application's performance, known as load testing.
Here, the load could be less than or equal to the desired load. It is used to analyze the user-friendliness and robustness of the software beyond the common functional limits. Primarily, stress testing is used for critical software, but it can also be used for all types of software applications.
To analysis, the application's performance by enhancing or reducing the load in particular balances is known as scalability testing. In scalability testing, we can also check the system, processes, or database's ability to meet an upward need. And in this, the Test Cases are designed and implemented efficiently. Stability testing is a procedure where we evaluate the application's performance by applying the load for a precise time.
It mainly checks the constancy problems of the application and the efficiency of a developed product. In this type of testing, we can rapidly find the system's defect even in a stressful situation.
Another type of non-functional testing is usability testing. In usability testing, we will analyze the user-friendliness of an application and detect the bugs in the software's end-user interface. Here, the term user-friendliness defines the following aspects of an application:. In compatibility testing, we will check the functionality of an application in specific hardware and software environments.
Once the application is functionally stable then only, we go for compatibility testing. Here, software means we can test the application on the different operating systems and other browsers, and hardware means we can test the application on different sizes.
Another part of manual testing is Grey box testing. It is a collaboration of black box and white box testing. Since, the grey box testing includes access to internal coding for designing test cases. Grey box testing is performed by a person who knows coding as well as testing. In other words, we can say that if a single-person team done both white box and black-box testing , it is considered grey box testing. The most significant part of Software testing is Automation testing.
It uses specific tools to automate manual design test cases without any human interference. Automation testing is the best way to enhance the efficiency, productivity, and coverage of Software testing.
In other words, we can say that whenever we are testing an application by using some tools is known as automation testing. We will go for automation testing when various releases or several regression cycles goes on the application or software. We cannot write the test script or perform the automation testing without understanding the programming language. In software testing, we also have some other types of testing that are not part of any above discussed testing, but those testing are required while testing any software or an application.
In smoke testing , we will test an application's basic and critical features before doing one round of deep and rigorous testing. Or before checking all possible positive and negative values is known as smoke testing. Analyzing the workflow of the application's core and main functions is the main objective of performing the smoke testing. It is used to ensure that all the bugs have been fixed and no added issues come into existence due to these changes.
Sanity testing is unscripted, which means we cannot documented it. It checks the correctness of the newly added features and components. Regression testing is the most commonly used type of software testing. We make an assumption that the second function could be the culprit so we comment it out. This debugging technique is only useable when we can comment out code and still have the rest of the code function properly.
If it is not possible to do this then a driver or stub might be what you need. A driver is a small piece of code which allows you to run another piece of code in a managed way. This can be useful for testing a function we have written. Lets say we have a function which accepts an array of integers as its parameter and sorts them in desending order. We have just written it and wish to test it as it is essential in the running of other code we wish to write next.
We want to run it in a managed way which will allow us to re run the tests several times easily as we iron out any bugs we find. Here is an example of what a driver might look like in this scenario:.
Picking the right tests to run is important in the effectiveness of your driver. You want to make sure you think of possible scenarios in which the code might break but also in which it does work to help you narrow down what the problem might be. A stub is the opposite of a driver. Whereas a driver sits above the code you have written and allows you to run it ie.
Stubs allow you to test code which relies on functionality which you have not yet implemented. Lets say you have written some code which prints the top 10 scores for a game you are developing. The scores will be stored in a database but you haven't written the functions for reading from the database yet.
Instead of calling the function which would read from the database to retrieve the data, we can instead call a function which will just return some static data for the purposes of testing if we can render the data properly.
Using the above techniques, we think we have narrowed down where the bug is in our code. Now we need to work out just what is happening. Desk checking involves manually working through a piece of code and tracking the values of variables as we go. It is a particularly effective debugging technique when we have a piece of code that is manipulating data. We perform our desk check as a table with the column headings being the names of the variables.
If we had the following code which finds the highest even number in an array of numbers:. Notice that the values within the columns have been entered with gaps where appropriate to show the chronological order of operations. This makes it easier to trace back through the processing if required. Sometimes it's useful to include columns which aren't variables but are specific bits of processing. The fourth column above isn't a variable but is processing that we want to ensure works properly so we have listed it as something to track.
There aren't any specific rules about what we do and don't enter for our columns but generally the more detail the better. All variables should be listed as a minimum though. Picking the right test data is important in maximising the value of desk checking. Sometime the processing will work under some conditions but not others. In general you want to make sure you check boundary values and differing scenarios.
When desk checking multiple sets of inputs you can check them in separate tables or to save yourself writing out the titles every time you can just list them on one table directly under each other. Separate the different tests with a horizontal line. Sometimes you can tell just by looking at the data what the result should be, sometimes you may need to work it out yourself. If you have created IPO tables for the processing as part of understanding the problem then referring to those can help you define the expected results.
Another technique to use once you think you know where the bug might be is to play about with a simpler piece of code in another file. Sometimes it is useful to verify different sections of code to help you narrow down where the problem is. If various sections of code may be run independently then copying that code to a separate file and testing it by itself can make it easier. You may need to add in some stubs, drivers and debugging output statements but it allows you to be certain that errors aren't being caused by something somewhere else in the code.
For instance, maybe we are making a game and the character is not responding to our button presses. Is the error in recording events keyboard presses , processing the actions or rendering the new data? It is done by comparing the data in the hypothesis with the original data to ensure that the hypothesis explains the existence of hints completely.
In case, the hypothesis is unable to explain the existence of hints, it is either incomplete or contains multiple errors in it. In this strategy, first step is to identify all the possible causes and then using the data each cause is analyzed and eliminated if it is found invalid.
Note that as in induction strategy, deduction strategy is also based on some assumptions. To use this strategy following steps are followed. Identifying the possible causes or hypotheses : A list of all the possible causes of errors is formed. Using this list, the available data can be easily structured and analyzed. Eliminating possible causes using the data: The list is examined to recognize the most probable cause of errors and the rest of the causes are deleted.
Refining the hypothesis: By analyzing the possible causes one by one and looking for contradiction leads to elimination of invalid causes. This results in a refined hypothesis containing few specific possible causes. Proving the hypothesis: This step is similar to the fourth step in induction method. This method is effectively used for locating errors in small programs.
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