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Black Box Testing: Types and Techniques

Black Box Testing is regarded as an influential technique that helps to examine the application under test (AUT). This arrangement is unique since the AUT is tested without stressing on the internal code structure or the implementation specifics related to the software. If we talk in a precise language, black box testing is primarily used to investigate the system against outside factors that are accountable for software failures.

The purpose of this testing approach is to keep a check on the input that enters the software, and the output which is formed. You need to be receptive and smart for the exact use of black-box testing. Some of the situations where black box testing techniques are required include:

  • To conveniently test software usability
  • Black box testing helps in examining the functionality of AUT
  • The testing approach sights the application directly through a user’s viewpoint
  • This testing provides an extensive outlook about the software
  • Black box testing examines the software/application as a complete system

Types of Black Box Testing

At present, the scope of Black Box Testing is immense and there are different types associated with it. Below mentioned are the three popular testing types that are usually preferred for a thorough examination of the software.

Functional Testing: This particular type focuses on the functional requirements associated with a system. Software testers are responsible for carrying out this test.

Non-Functional Testing: This black box testing type doesn’t associate with any specific functionality. It is solely focused on non-functional requirements like usability, scalability and performance.

Regression Testing: Black box testing also includes Regression Testing that is conducted after code fixes or system upgrades. The motive is to examine the new code and its effect on the existing code.

Various Techniques for Black Box Testing

Looking at the range of black-box testing its approach involves several techniques. Below mentioned are some of the prominent techniques that are used for fulfilling software testing needs.

Boundary Value Analysis

This is a popular black-box testing technique that also functions as the base to equivalence testing. The use of Boundary Value Analysis is centred at testing the software using test cases that involve extreme values of test data. To be precise, BVA helps identify errors that can shoot as a result of restrictions of input data.

State Transition Testing

Under this testing technique, the testers make use of inputs, outputs as well as the state of the system all along with the testing phase. State Transition Testing examines the software in relation to a sequence of transitions occurring within the test data.

It totally depends on the type of software/application that needs to be tested. State Transition Testing will look out for behavioural changes in the system on the whole.

Equivalence Partitioning

The approach for this testing technique is quite exquisite as it examines the input and output through segregating the input into equal classes. As per Equivalence Partitioning, the data needs to be examined at least once in order to ensure maximum test coverage. You can view it as a comprehensive testing method that further reduces the repetition of inputs.

Decision Table Testing

Test cases created under Decision Table Testing are based on different possibilities. A decision table format is structured for the various test cases to check each condition and offer a correct output. This is suitably preferred when there are multiple input combinations and possibilities.

Advantages of Black Box Testing

For testers, Black Box Testing is a helpful method since it provides multiple advantages. Not only the quality of the software, but the correct implementation of black-box testing tools and techniques can also help achieve reliability, efficiency and a lot more.

Below mentioned are the main benefits of black-box testing:

  • Testing is conducted post the development stage; both processes are independently performed.
  • There is no requirement of technical knowledge of carrying out black-box testing.
  • Black box testing works perfectly with functional testing services and implementations.
  • Test cases can be structured before development and even after the specification check.
  • Black box testing is basically for an extensive coverage that is generally skipped by most testers
  • It is believed that the Black box testing approach is somewhat similar to Agile.

Disadvantages of Black Box Testing

  • Operating with a broad sample set of test inputs might get really exhausting.
  • The possibility of unclear results and dead ends are fairly high.
  • If you lack precise and well-defined specifications, there can be trouble in designing test cases.
  • Black box testing results can at times be overestimated.
  • Black-box testing techniques can end up being redundant if the software developer has already implemented them.

Maintaining quality for a software or application requires strict supervision and testing procedures. Black box testing offers a simplified version of investigation without any necessary knowledge of technical terminologies. Being a renowned software testing company, ImpactQA is well-equipped to perform such tests with the utmost ease. You can connect with testing experts for attaining information about the various approaches of black-box testing.

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How Does Big Data Testing Impact an Organization?

We cannot deny the fact that data is the gold stock for an organization without which its sustenance is dim. Big Data, known as extremely large sets of data acquired from multiple sources is beneficial for extracting valuable information. This is the reason why every organization is focused to implement the right techniques for the collection, storage, assessment and testing of Big Data.


What is the importance of Big Data testing? It primarily helps to eliminate data complexities through the confirmation of the quality and integrity of your data. Here are three essential pointers that support the vital nature of big data testing in today’s highly competitive world.  


  • High-quality assessment of data is listed as one of the topmost preferences for business organizations. It has become imperative to utilize big data by large organizations to acquire tested and affirmed insights.  
  • For instance, if big data does not undergo a quality test, it might become difficult to provide useful information that can support an organization with decision-making tasks.
  • Even though numerous enterprises organize comprehensive Big Data analysis, there are cases when they fail to obtain the desired result. Under such circumstances, the only smart way to ensure the correct processing and assessment of data is through the implementation of Big Data testing.


Must Read: How Big Data can be leveraged in all aspects of Testing?


Applicability of Big Data Testing for an Organization


Processing of big data can be tricky and does require a structured plan which shouldn’t limit itself to just data validation but also lay emphasis on system testing and other aspects. To overcome this issue and distribute efforts evenly across segments, there are different methods to operate on big data.


  • Testing Scheme:

Frame better testing actions that automate the procedure concerned with the collection and examination of valid data. It is essential to make sure that the test automation scheme runs parallel to the stated business targets.


  • Functional Testing:

While reviewing every aspect of Big Data, functional testing is required to authorize the outcomes. It becomes crucial to perform and validate at each stage with a purpose to eradicate defects and fulfil customer expectations.


  • Performance Testing:

Assessing the performance to measure the speed, stability, and adaptability concerning different data sets. This action involves the analysis of large data under a short period. To be specific, the goal is to highlight defects and remove the blockers which are known to hamper performance.




Benefits of Enterprise-Level Big Data Testing


Apart from the above-mentioned assistance, Big Data testing does prove advantageous in several other ways. Here are 5 such profitable facts of Big Data testing impacting your enterprise:


  • Slashed Downtime

It is a fact that several applications rely on data for active operations. As for bad data, it tends to hamper the performance and effectiveness of the application. There are cases when during the collection and distribution of data on applications, organizations aren’t able to analyze data health that results in downtime. A smart solution to this issue is Big Data testing which can help modify data quality and associated application processes that eventually reduce the overall downtime.


  • Better Market Strategies

Currently, businesses are concentrated on extracting big data benefits to plan their digital marketing schemes. As web technology advances, it has become convenient for businesses to gather huge amounts of data based on history and user activity. This data can be converted into a persuasive, individualized experience for every customer who visits the website. In short, big data testing can assist businesses in setting up optimization targeting to make improved decisions.


  • Enhanced Data Security

For enterprises involved with client applications, it is vital to maintain data confidentiality to sustain the level of trust established by their clients. In case of a breach in data security, the brand name falls in the risk zone. Therefore, it is suggested to conduct big data testing at all levels to avert the chances of data security failure.


Big Data Testing & Security Concerns (Image Source)



  • ROI Improvement

When it comes to enterprises, it is necessary to get competitive while structuring strategies for Predictive Analytics and Big Data. The addition of testing as a compulsory activity right before any analysis or processing ensures that the organization is dealing with the correct data to expect better outputs. Such an execution is a fine example of uplifting the ROI and take a significant lead over competitors.


  • Controlled Consistency

The use of different applications by enterprises is a routine act for dealing with diverse data sets which can cause data inconsistencies. It is surely a big disgrace to organizations when the acquired output through Predictive Analytics and Big Data does not show consistency. It is through Big Data testing that variability in data can be determined beforehand to permit suitable actions for discarding the uncertainty.  


This write-up gives a brief outlook as to why Big Data testing is staged upfront for managing Big Data systems. Inappropriate testing of Big Data systems directly affects business, which is something that needs to be addressed first hand. The incorporation of Big Data Testing is basically a correction method that ultimately avoids the wastage of resources.


Big Data testing and its effective impact to an organization can be further understood through expert guidance at ImpactQA. The team of testers and developers are always ready to resolve your doubts associated with testing Big Data for diverse processes.

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