Software Testing Techniques: Types and Best Practices

Software Testing Techniques Based on Codebase

Software testing techniques are broadly classified into two main categories based on the running of the codebase. Those are as follows:

• Static testing
• Dynamic testing

Static Testing

Static testing is mainly used to find the error or flaw in the software application without actually running the code. In other words, static testing is a software testing approach that involves reviewing and analyzing software documentation, design, or code without executing the software. It’s a method of checking for errors and defects in the software by inspecting it in a static state rather than during active operation.

This technique is implemented early in the Software Development Life Cycle (SDLC). By identifying defects early, static testing helps enhance the overall quality and reliability of the software application. Furthermore, it improves maintainability and leads to long-term time and cost savings.

Static testing includes two techniques:

• Reviews
• Static Analysis

Reviews

Reviews are a crucial aspect of static testing, ranging from informal peer reviews between developers/testers on artefacts (code/test cases/test data) to formal inspections. They allow testers to identify defects and issues in documentation, such as requirements and design, at an early stage. Reviews are executed manually by various project members, including architects, designers, managers, moderators, and reviewers.

The following are the different types of reviews:

• Informal reviews: Quick and effective code or documentation review by another developer or team member. Feedback is captured without generating a report, and necessary implementations are made.
• Walkthroughs: Authors and other associates gather to discuss software defects. Questions, comments, and answers are exchanged until all members are satisfied, leading to conclusions.
• Technical reviews: Formal review by a trained moderator or technical expert. Qualified personnel review the software application for discrepancies from specifications and standards, focusing on technical concept corrections and identifying alternatives if necessary.
• Inspections: Formal type of review that follows a strict process to detect defects. Reviewers use a checklist to review the software product, and a trained moderator leads the meeting. Each defect is recorded, and participants are informed to rectify errors.

Static Analysis

Static analysis, a software testing technique, involves reviewing code without executing it. Essentially, it’s a form of code analysis performed to understand the code structure and adhere to coding standards. Mainly, it is regarded as the debugging process that aims to test the source code to find any structural error in the developer’s code:

• Unused variables
• Syntax violations
• Non-compliance with coding standards
• Dead code

Developers commonly utilize code analysis tools like PyCharm, Checkstyle, and SourceMeter to analyze code and identify issues, which helps detect structural issues and implement coding standards.

The following are the different types under static analysis:

• Data flow analysis: This technique verifies the data structures, variable definitions, and their usage without code execution, focusing on where variables are defined and used to identify and eliminate potential issues that could disrupt data flow, resulting in program malfunctions or incorrect outputs.
• Control flow analysis: This technique analyzes the control flow of the programming language, defined in a control flow graph, to verify the functions, modules, and subroutines and ensure the correctness of process flows.
• Cyclomatic complexity: This technique identifies how many different directions a software application can go, and it creates test cases to explore each direction effectively. This makes testing more efficient and thorough.

Dynamic Testing

Dynamic testing involves analyzing the dynamic behavior of code within a software application. During this process, it’s crucial to provide input and output values as expected when executing a test case. This can be done manually or through automation. Additionally, compiling and executing the software code is necessary.

The main objective of dynamic testing is to validate the software, ensuring it functions correctly without any defects post-installation. Essentially, dynamic testing verifies the overall functionality and performance of the software application, aiming for stability and consistency.

Dynamic testing includes three techniques:

• White box testing
• Black box testing
• Grey box testing

White Box Testing

White box testing involves verifying the internal structure and code of the software. This necessitates coding expertise as it includes evaluating the internal code implementation and algorithms of software applications. Testers have access to the underlying code and leverage this knowledge to create test cases targeting specific code paths, logic branches, and conditions. In this testing approach, the code is executed line by line to identify any associated errors and evaluate the performance of software applications based on the code.

Following are the different software testing techniques under white box testing:

• Statement coverage: A primary objective of white box testing is to have a maximum portion of the source code. Code coverage indicates the extent to which an application’s code is subjected to unit tests that verify its functionality.

By using concepts like statement coverage, branch coverage, and path coverage, it becomes possible to evaluate the extent to which an application’s logic is executed and validated by the unit test suite.

Statement coverage=(Total number of statements/Number of executed statements)×100

• Branch coverage: In programming, a branch refers to a point in the code where the program’s execution flow can diverge and follow different paths based on certain conditions. Consequently, in branch coverage, we verify whether each branch is executed at least once.

In the case of an if statement, two test conditions arise: one is used to validate the “true” branch, whereas the other is used to validate the “false” branch.

Branch coverage=(Total number of branches/Number of executed branches)×100

• Path coverage: In this software testing technique, all the paths within the software application are explicitly tested. This technique ensures that each potential application path is traversed at least once, offering more effectiveness compared to branch coverage. It’s especially valuable for testing intricate applications.
• Decision coverage: It is a technique that verifies the true or false outcomes of every boolean expression in the source code. Decision coverage aim is to cover and validate all available source codes by ensuring that each branch of every potential decision point is visited at least once.

A decision point arises when multiple outcomes are possible from control flow statements like if statements, do-while statements, or switch case statements.

• Condition coverage: This software testing technique is also known as expression coverage. It evaluates the variables or sub-expressions in a conditional statement. Its goal is to evaluate the outcome of each logical condition. This coverage focuses solely on expressions with logical operands, where an operand outputs either true or false. However, it doesn’t ensure complete decision coverage.
• Multiple condition coverage: This testing method evaluates all possible combinations of conditions for each decision.

For example, consider the expression:

if (A||B)
then
print C

In this scenario, the test cases would be:

TEST CASE1: A=TRUE, B=TRUE
TEST CASE2: A=TRUE, B=FALSE
TEST CASE3: A=FALSE, B=TRUE
TEST CASE4: A=FALSE, B=FALSE

Note that in this example, with 2 expressions, A and B, there are 4 test cases. Similarly, for 3 conditions, there would be 8 test cases, following the general formula: for n conditions, there will be 2n test cases.

• Finite state machine coverage: It is challenging to achieve a finite state machine due to its focus on the design’s functionality. This approach involves tallying the frequency of state visits or transitions and determining the number of sequences within a finite state system. In a finite state machine, a sequence represents an ordered list of inputs or outputs.
• Control flow testing: This testing technique aims to ascertain the program’s execution sequence using its control structure. Test cases are formulated based on the program’s control structure, and a specific section of the program is selected to construct the testing path.

It is mainly used in unit testing, and test cases are depicted using the program’s control graph. The control flow graph comprises nodes, edges, decision nodes, and junction nodes for all execution paths.

Black Box Testing

Black box testing involves validating the software application without access to its internal structure, code, or design. Thus, programming expertise is not required for this testing method. Its goal is to confirm the functionality of software applications under test and execute the entire test suite without programming knowledge, adhering to requirements and specifications.

In black box testing, the software is treated as a “black box,” testing is based solely on inputs and expected outputs without considering internal functions. For instance, inputting values into a system categorizes it into different classes or groups based on similar outcomes to those values. Consequently, one value can be used to test the outcomes of multiple groups or classes, known as Equivalence Class Partitioning (ECP), which is a black box testing technique.

Following are the different software testing techniques under black box testing:

• Boundary value analysis: This testing technique focuses on values at the boundaries of input ranges to verify if a system handles extreme values within acceptable ranges correctly. By testing values like the minimum, maximum, and just beyond these boundaries, testers can uncover potential issues related to boundary conditions. It proves particularly beneficial when inputs are anticipated to fall within specific ranges, such as age limits or numerical constraints.

For instance, testing a software application accepting integers between 1 and 100 would involve boundary values like 1 and 100 and values slightly outside the range, such as 0 and 101.

There are two types of boundary testing:
• Inner boundary testing: It focuses on input values just inside the input domain, including the minimum and maximum allowed values.
• Outer boundary testing: It focuses on input values slightly outside the input domain, such as values slightly above or below the minimum and maximum allowed values.
Boundary analysis is a crucial technique for identifying errors and ensuring proper application behavior for all inputs, not solely those within the middle of the input domain. It helps detect and rectify bugs that might otherwise remain unnoticed.
• Equivalence class partitioning: This technique functions as a method in software testing for dividing potential inputs into sets of equivalence classes, aiming to identify and test a representative selection of inputs from each class. Each element within an equivalence class should produce the same output, simplifying testing to just one value from each set.
This approach assists in minimizing the number of required test cases while maintaining effective test coverage. Equivalence classes are established based on functional or non-functional requirements, considering factors like data types, ranges, and relationships among input values.
Let’s take an example. Suppose an optional field accepts integer values ranging from 1 to 10. Then, the equivalence classes would include:
• No value (valid partition)
• Values between 1 and 10 (valid partition)
• Values smaller than 1 (invalid partition)
• Values higher than 10 (invalid partition)
Thus, only four test cases are needed—one for each partition. Retesting the same partition with multiple values yields no additional insights; testing value 5 would provide similar outcomes for values 4, 8, etc.
• Decision table-based testing: This technique is used to visually illustrate various combinations of inputs and outputs, focusing more on business rules. It’s employed in scenarios where diverse combinations of test input conditions yield different outputs.
Using this method, we identify the conditions and resulting actions of the testing object and present them in a table format. Typically, conditions represent the inputs, usually located at the top, while actions denote the outputs, usually defined at the bottom of the table. The first column of the table explains the conditions and actions, with each subsequent column representing a decision rule. When a condition or action is TRUE, denoted by Y, T, or 1, and when FALSE, denoted by N, F, or 0. If the value doesn’t matter, we use – or N/A.
There’s always an option to simplify the table by removing columns that don’t affect the outcome or those with impossible combinations of conditions. To achieve 100% coverage, each rule in the table needs to be tested at least once.
• State transition testing: This technique ensures software application behaves as expected during transitions between different system states. Initially, it identifies a finite set of states the software application can occupy and then tests how it transitions between them based on varying input conditions. This technique applies to various systems, such as vending machines, traffic lights, and web applications, provided their behavior can be defined as a finite state machine.
Here are some of the state transition testing techniques:
• Cause-effect graphing: It identifies and tests various input combinations leading to specific outputs or events.
• Stateflow: It models and simulates state-based systems that are useful for developing and verifying state-based algorithms and control logic.
• State transition diagram: It is a graphical representation of the system’s states and the events causing transitions between them.
• State transition table: It is a tabular representation of the system’s states, events leading to state transitions, and resulting actions or outputs.
• Pairwise testing: This technique is used where all possible pairs of input parameters are tested together. It creates test cases that cover every pair of combinations of input values for a specific set of parameters. It is used when a large number of inputs are present, making it challenging to test every possible combination separately.
Think about a software application containing 3 sections, A, B, and C, where each section can select from 3 values: 1, 2, or 3. Using conventional testing techniques would require testing each of the 27 (3^3) possible combinations separately, which is a time-consuming and inefficient process.
• Error guessing: This technique is used in software testing to discover possible defects or errors. Testers use their expertise, domain knowledge, and intuition to predict and discover flaws in software applications. This method goes beyond formal test cases and planned scenarios, allowing testers to mimic users’ views and anticipate the software application’s performance. This type of testing adds a human element to the process and promotes analytical thinking, improving application quality by finding errors that may not be caught otherwise.

Grey Box Testing

Grey box testing involves integrating aspects of both white box and black box testing. Here, testers engage in both types of testing procedures, hence the term “grey box.” In this approach, testers have partial visibility into the internal coding, resembling a semi-transparent grey box.

A practical illustration of grey box testing is evident in database testing. Here, evaluation includes both the front-end and back-end elements of the database. The front end involves user interface (UI) operations, such as login functionalities. No programming knowledge is required in this context, aligning with black box testing principles.

Conversely, the back-end pertains to the internal architecture of the database, necessitating an understanding of table structures. By writing and executing SQL commands, testers can verify modifications within the tables, similar to white box testing practices. Through this combined approach, grey box testing is effectively performed.

Following are the different software testing techniques under grey box testing:

• Matrix testing: It involves evaluating business and technical risks developers identify in software applications. Developers specify all program variables, each carrying inherent technical and business risks and potentially utilized with varying frequencies throughout its lifecycle.
• Pattern testing: It involves analysis to determine the root causes within the code. The analysis template includes defect reasons, facilitating proactive identification of potential failures before production.
• Orthogonal array testing: It involves numerous permutations and combinations of test data. It is used when maximal coverage is necessary with few test cases and extensive test data, proving valuable in evaluating complex software applications.
• Regression testing: It involves evaluating software applications after each change to ensure that modifications or new functionalities do not adversely impact existing software application workings. It also confirms that rectifying defects does not disrupt other software functionalities.
• API testing: It involves testing the software applications’ exposed interfaces. The primary aim is to verify that the API accepts various input formats and functions as intended.

Software Testing Techniques Based on Approach

Software testing techniques are broadly classified into two main categories based on the approach. These are as follows:

• Manual Testing
• Automated Testing

Manual Testing

Manual testing refers to the process of testing software applications by human testers, who carefully execute test cases without using testing tools. In manual testing, testers simulate end-user behavior to ensure that the software functions correctly and meets the specified requirements.

During manual testing, testers follow predefined test cases and execute various test scenarios to identify software application bugs, defects, or inconsistencies. They document their findings, including any defects discovered during the testing process, to facilitate communication with developers for resolution.

Characteristics:

• It is particularly effective for testing user interfaces and experience, as testers can directly interact with the UI elements to assess usability, accessibility, and responsiveness.
• It helps detect usability issues such as navigation difficulties, confusing workflows, and inconsistent design elements, which might not be easily detectable through automated testing alone.
• It is highly flexible and can quickly adapt to changes in requirements, making it suitable for dynamic and evolving projects where automated tests may be impractical or inefficient.
• It can provide subjective software quality assessments, offering insights beyond what automated tests can detect. They can evaluate aspects like user experience, aesthetics, and intuitiveness.

Automated Testing

Automated testing refers to the process of automating repetitive test cases to ensure the software meets requirements before release, eliminating the need for human testers. Automated software testing uses scripted sequences executed by testing tools. These tools evaluate the software, report outcomes, and compare results with previous test runs. Once created, an automated test script can be reused multiple times.

Characteristics:

• It involves the creation and execution of test scripts or scenarios using specialized tools or frameworks. These scripts automate the process of interacting with the software application and verifying its behavior.
• It can be executed in parallel across multiple environments, further enhancing testing efficiency and scalability. Parallel execution reduces test execution time and allows for the testing of different components or features.
• It enables comprehensive test coverage by executing a large number of test cases across different scenarios, configurations, and environments. This helps identify defects early in the development lifecycle and improve overall software quality.
• It can significantly reduce the time and effort required for testing, especially for repetitive tasks, large-scale testing, or projects with frequent releases. Automated tests can run much faster than manual tests, accelerating the testing process.
• It supports data-driven testing approaches where test scenarios are executed with different sets of input data to validate various use cases and edge conditions. This enhances test coverage and helps identify potential data handling and processing issues.
• It can be integrated into CI/CD pipelines, allowing for continuous testing and validation of code changes throughout development. This integration helps detect and address issues early, leading to faster delivery of high-quality software.

Whether manual or automation testing, there are two main ways to implement them: on-premise and cloud-based. On-premise testing involves running the software applications on local machines. This can be a real pain in terms of scaling up the testing process, keeping everything maintained and monitored, and installing software upgrades. All this stuff requires a lot of teamwork and can be quite time-consuming and expensive.

An ideal option here is to choose cloud-based testing platforms such as TestMu AI. It is an AI-powered test orchestration and execution platform that allows manual and automation testing across 3000+ real devices, browsers, and OS combinations. TestMu AI supports various test automation frameworks and tools, providing both web testing and mobile app testing needs. It seamlessly integrates with Selenium, Playwright, Cypress, Appium, and more.

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