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TESTING CONCEPTS PDF

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myavr.info or this tutorial may not be redistributed or reproduced in any way, .. This chapter describes the basic definition and concepts of Testing from . 5. What is software? Why should it be tested? Software Testing Levels, Types, Terms and Definitions. In this way, it uses the same concept of partitions as. Testing is the process of evaluating a system or its component(s) with the intent This tutorial will give you a basic understanding on software testing, its types.


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White Box Testing techniques are also known as Open Box Testing / Glass Box. Testing / Clear Box This model defines the conceptual mapping in between. PDF | On Jul 1, , Ali Mili and others published Software Testing: Concepts and Operations. "This course teaches you basic to advance level concept in software testing. Learn to jump-start your testing career ".

The front end can be a web based application which interfaces with Hadoop or a similar framework on the back end. Results produced by the front end application will have to be compared with the expected results in order to validate the application. Functional testing of the applications is quite similar in nature to testing of normal software applications.

Since the schema may change as the application evolves, the software tester should be able to work with a changing schema. Since the data can come from variety of data sources and differ in structure, they should be able to develop the structure themselves based on their knowledge of the source. This may require them to work with the development teams and also with the business users to understand the data.

In general applications the testers can use a sampling strategy when testing manually or an exhaustive verification strategy when using an automation tool.

However in case of big data applications since the data set is huge even extracting a sample which represents the data set accurately, may be a challenge. Testers may have to work with the business and development team and may have to research the problem domain before coming up with a strategy Testers will have to be innovate in order to come up with techniques and utilities that will provide adequate test coverage while maintaining high test productivity.

In some organizations, they may also be required to have or gain basic knowledge of setting up the systems. They may also be called upon to write MapReduce programs in order to ensure complete testing of the application. Testing of big data application requires significant technical skills and there is a huge demand for tester who possess these skills.

This data can be in terms of petabytes or more. Hadoop can easily scale from one node to thousands of nodes based on the processing requirements and data.

Reliable : Big data systems are designed to be fault tolerant and automatically handle hardware failures. Hadoop automatically transfers tasks from machines that have failed to other machines.

Economical : Use of commodity hardware along with the fault tolerance provided by Hadoop, makes it a very economical option for handling problems involving large datasets. Flexible : Big data applications can different types of heterogeneous data like structured data, semi structured data and unstructured data.

It can process data extremely quickly due parallel processing of data. Each component of the system belongs to a different technology. The overheads and support involved in ensuring that the hardware and software for these projects run smoothly, is equally high. Logistical Changes — Organizations that want to use big data may have to modify how data flows into their systems. They will have to adapt their systems to constant flow of data rather than in batches. This could translate to significant change to their existing IT systems.

Skilled Resources — Testers and developers who work on big data project need to be highly technical and skilled at picking up new technology on their own. Finding and retaining highly skilled people can be a challenge. Expensive — While big data promises use of low cost machinery to solve computing challenges, the human resources required in such projects are expensive.

Data mining experts, data scientists, developers and testers required for such projects cost more than normal developers and testers. Accuracy of Results — Extracting the right data and accurate results from the data is a challenge. Example: Gmail can sometimes mark a legitimate email as spam. If many users mark emails from someone as spam, gmail will start marking all the emails from that sender, as spam. Hadoop Architecture Hadoop is one of the most widely used frameworks in big data projects.

Though testers may be interested in big data from a testing perspective, it is beneficial to have high level understanding of the Hadoop architecture. The above diagram shows the high level architecture of Hadoop. Hadoop is installed on client machines and they control the work being done by loading cluster data, submitting MapReduce jobs and configuring the processing of data.

Types of Software Testing: Different Testing Types with Details

They are also used to view results. All of these machines together form a cluster. There can be many clusters in the network. MasterNodes have two key responsibilities.

First, they handle distributed storage of data using NameNodes. Second, parallel processing of data MapReduce which is coordinated by JobTracker.

Secondary NameNode acts as a backup NameNode. Slave nodes form the bulk of the servers. They store and process the data. Each slave node has a DataNode and a TaskTracker.

The DataNode is a slave of and receives instructions from the NameNodes and carries out storage of data as shown below. The TaskTracker is a slave to and receives instructions from JobTracker. It processes the data using MapReduce which is a two step process. The workflow of Map process is shown below. The sequence of events of the Reduce process is shown below. It is used for distributed processing and storage of large datasets using clusters of machines. It can scale from one server to thousands of servers.

It provides high availability using cheap machines by identifying hardware failures and handling them at application level.

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MapReduce — MapReduce is programming model for parallel processing of large data sets Hive — Apache Hive is data warehouse software that is used for working with large datasets stored in distributed file systems HiveQL — HiveQL is similar to SQL and is used to query the data stored in Hive. HiveQL is suitable for flat data structures only and cannot handle complex nested data structures. Pig Latin can be used to handle complex nested data structures. Pig Latin is statement based and does not require complex coding.

Commodity Servers — When working with big data, you will come across terms like Commodity Servers. This refers to cheap hardware used for parallel processing of data.

This processing can be done using cheap hardware since the process is fault tolerant. If a commodity server fails while processing an instruction, this is detected and handled by Hadoop. Hadoop will assign the task to another server. This fault tolerance allows us to use cheap hardware.

Node — Node refers to each machine where the data is stored and processed. Big data frameworks like Hadoop allow us to work with many nodes. Nodes may have different names like DataNode, NameNode etc.

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DataNodes — This are the machines which are used to store data and process the data. NameNodes — NameNode is the central directory of all the nodes. Master Nodes — Master nodes which oversee storage of data and parallel processing of the data using MapReduce. It uses NameNode for data storage and JobTracker for managing the parallel processing of data.

JobTracker — It accepts jobs, assigns tasks and identifies failed machines Worker Nodes — They are the bulk of virtual machines and are used for storing and processing data. Each worker node runs a DataNode and TaskTracker — which is used for messaging with the master nodes. Client Nodes — Hadoop is installed on client nodes. They are neither master nor worker nodes and are used to setup the cluster data, submit MapReduce jobs, view the results.

Clusters — A cluster is a collection of nodes working together.

These nodes can be master, worker or client nodes. Big Data Automation Testing Tools Testing big data applications is significantly more complex than testing regular applications. Big data automation testing tools help in automating the repetitive tasks involved in testing. Any tool used for automation testing of big data applications must fulfill the following needs: Allow automation of the complete software testing process Since database testing is a large part of big data testing, it should support tracking the data as it gets transformed from the source data to the target data after being processed through the MapReduce algorithm and other ETL transformations.

Beta testing is successful when the customer accepts the software. Usually, this testing is typically done by end-users or others. It is the final testing done before releasing an application for commercial purpose.

Usually, the Beta version of the software or product released is limited to a certain number of users in a specific area. So end user actually uses the software and shares the feedback to the company. Company then takes necessary action before releasing the software to the worldwide.

Database testing involves testing of table structure, schema, stored procedure, data structure and so on. In back-end testing GUI is not involved, testers are directly connected to the database with proper access and testers can easily verify data by running a few queries on the database.

There can be issues identified like data loss, deadlock, data corruption etc during this back-end testing and these issues are critical to fixing before the system goes live into the production environment 7 Browser Compatibility Testing It is a subtype of Compatibility Testing which is explained below and is performed by the testing team.

Browser Compatibility Testing is performed for web applications and it ensures that the software can run with the combination of different browser and operating system. This type of testing also validates whether web application runs on all versions of all browsers or not. Backward Compatibility Testing checks whether the new version of the software works properly with file format created by older version of the software; it also works well with data tables, data files, data structure created by older version of that software.

If any of the software is updated then it should work well on top of the previous version of that software. Tests are based on the requirements and functionality. Detailed information about the advantages, disadvantages, and types of Black box testing can be seen here. Boundary value Testing is performed for checking if defects exist at boundary values.

Boundary value testing is used for testing a different range of numbers. There is an upper and lower boundary for each range and testing is performed on these boundary values. If testing requires a test range of numbers from 1 to then Boundary Value Testing is performed on values at 0, 1, 2, , and Branch Testing, the name itself suggests that the code is tested thoroughly by traversing at every branch.

Compatibility testing ensures that software can run on a different configuration, different database, different browsers, and their versions. Compatibility testing is performed by the testing team. Component Testing involves testing of multiple functionalities as a single code and its objective is to identify if any defect exists after connecting those multiple functionalities with each other.

During this equivalence partitioning , a set of group is selected and a few values or numbers are picked up for testing. It is understood that all values from that group generate the same output. The aim of this testing is to remove redundant test cases within a specific group which generates the same output but not any defect.

So the Equivalence Partitioning for this testing is: to -1, 0, and 1 to Example testing includes the real-time scenario, it also involves the scenarios based on the experience of the testers.

The objective of this testing is to explore the application and looking for defects that exist in the application. Sometimes it may happen that during this testing major defect discovered can even cause system failure. During exploratory testing, it is advisable to keep a track of what flow you have tested and what activity you did before the start of the specific flow. An exploratory testing technique is performed without documentation and test cases.

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It is a Black-box type testing geared to the functional requirements of an application. For detailed information about Functional Testing click here.

The GUI testing includes the size of the buttons and input field present on the screen, alignment of all text, tables and content in the tables. It also validates the menu of the application, after selecting different menu and menu items, it validates that the page does not fluctuate and the alignment remains same after hovering the mouse on the menu or sub-menu.

In Gorilla Testing, one module or the functionality in the module is tested thoroughly and heavily. The objective of this testing is to check the robustness of the application. It does not look for negative or error conditions. The focus is only on the valid and positive inputs through which application generates the expected output. Application functionality and modules should be independent enough to test separately.

This is done by programmers or by testers. Modules are typically code modules, individual applications, client and server applications on a network, etc. Load testing helps to find the maximum capacity of the system under specific load and any issues that cause the software performance degradation. Monkey Testing is performed randomly and no test cases are scripted and it is not necessary to Monkey Testing is performed randomly and no test cases are scripted and it is not necessary to be aware of the full functionality of the system.

The change in the program source code is very minimal so that it does not impact the entire application, only the specific area having the impact and the related test cases should able to identify those errors in the system.

A negative testing technique is performed using incorrect data, invalid data or input. It validates that if the system throws an error of invalid input and behaves as expected. The objective of NFT testing is to ensure whether the response time of software or application is quick enough as per the business requirement. It should not take much time to load any page or system and should sustain during peak load.

Performance Testing is done to check whether the system meets the performance requirements. Different performance and load tools are used to do this testing. Recovery testing determines if the system is able to continue the operation after a disaster.

Assume that application is receiving data through the network cable and suddenly that network cable has been unplugged.Sir you are just fabulous I just read ur sdlc model what a expiation just superb ur too good yaar love you. Software Testing Tutorials 16 Lessons Software Testing Overview Software testing can be defined as the action for checking if the tangible result or output of product matches with the projected or expected output of your client and testing also ensures that the product is free from any bug or defect.

In back-end testing GUI is not involved, testers are directly connected to the database with proper access and testers can easily verify data by running a few queries on the database. Beta testing is carried out to ensure that there are no major failures in the software or product and it satisfies the business requirements from an end-user perspective.

Installation 2.

DataNodes — This are the machines which are used to store data and process the data. This can in turn impact the success of the project.

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