How AI is bringing change to the software testing practice
Artificial Intelligence is penetrating into multiple functions performed by the software industry. In software testing, the technology holds the potential to be a game-changer. Imagine the capability of your software to test and diagnose itself and make self-corrections! This will lead to huge savings on your resources. With this in mind, let’s try and understand exactly how AI will impact the traditional way of software testing.
Before we proceed, let’s get one thing clear – Do we really need AI in software testing?
Do We Really Need AI in Software Testing?
Software testing came into existence as a result of the evolution of development methodologies. It fed the need for robust, error-free software products. Testing was a laborious task for sure. However, automating software testing required traceability and versioning, both of which were critical and needed careful consideration. Something was needed to resolve this.
As businesses move towards digital transformation and the software market continues to grow, businesses expect a real-time risk assessment across all stages of the software delivery cycle. AI in software testing is the right response to these challenges. AI can develop error-free applications while enabling greater automation in software testing. This helps meet the expanded, critical demands for testing. It improves the quality of engineering and reduces testing time allowing the tester to focus on more important things. The verdict is clear then – We Really Need AI for Software Testing!
Five Impressive Ways AI Impacts Software Testing
1. Improves object application categorization
AI is widely used in object application categorization. When tools and testers are created, unique pre-train controls can be created. Once the hierarchy of the controls is categorized, testers can create a technical map to obtain labels for the different controls.
In the near future, AI will become capable of observing users perform exploratory testing on the testing site. And once user behavior is assessed, it can assign, monitor, and categorize the risk preference.
2. Automation of test case writing
Gone are the days of web crawlers. As automation is picking momentum, AI tools have become capable of learning business usage scenarios of test applications.
Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value
They can automatically collect insightful data such as HTML pages, screenshots and page loading time and eventually train ML models for expected patterns of the app. And as soon as they are executed, any variations are marked as potential issues. This makes it easier for the tester to find and validate differences and fix issues.
3. Enhanced accuracy
To date, source analysis requires human resources to accomplish the task. Unfortunately, because of the enormity of the data, even the best experts could overlook, or miss out on observing certain critical defects. Human error and the tendency to lose focus further impairs the experts involved in software testing. It can be disastrous if bugs caused by these errors are caught by consumers before project stakeholders. Product positioning and brand reputation can be jeopardized.
Thankfully, AI can teach systems to learn source analysis and, in the future, apply this acquired knowledge. This ensures that testers have greatly enhanced accuracy. It eliminates the probability of human error and also shortens the time to run a test and increases the possibility of finding defects or bugs.
4. Automation without the user interface
AI-based techniques can be applied for non-functional tests such as performance, security and unit integration. It can also be applied on various application logs which assists in developing auto-scaling capabilities such as bug prediction.
AI algorithms can enhance UI testing, predict the next test, determine the outcomes for subjective and complex tests and much more. In other words, AI could increase the overall test coverage while it increases the depth and scope of the test itself.
5. Reduces cost and decreases time to market
The need for manually repeating a test is time-consuming and extremely expensive. But with AI, such tests can be automated to repeat several times over. Each time the software test is repeated automatically, the source code gets modified to correct any bugs. This eliminates the additional cost of repeating the test and increases the speed of the test from days to hours, which in turn saves more money.
Related Reading: Quality Assurance in Software Testing – Past, Present & Future
Allow AI to Revolutionize your Business
AI has proven to have a significant impact on software testing with its benefits ranging from optimization to extraordinary savings. It enables testers to move beyond the traditional route and dive toward precision-based testing processes. This can prove invaluable to your business. To find out how you can make this happen for your business, contact us.
What 2020 holds in store for software testing automation
Enterprises are constantly on the hunt for trends that improve their application’s effectiveness, expedite development cycles, reduce downtimes, and improve cost savings. In such cases, they often resort to methods that can automate mundane testing procedures. Why is it the right time to leverage test automation trends? Here we discuss a few software testing automation trends expected to dominate in 2020.
The automation testing lifecycle
Automation testing provides testing teams the ability to improve the coverage of their software tests and additionally offers improved quality, cost savings, shorter testing cycles, and a multitude of other benefits. To reap benefits out of automation techniques, the key is to incorporate the right skills, planning, as well as the right testing tools.
Let us walk through the various phases involved in an automation lifecycle:
- When and what and how to automate?
- Selecting the right tool
- Test planning, designing, and strategy selection
- Establishing the testing environment
- Development and execution of the automation test script
- Report Generation
Related Reading: Understanding The Different Types Of Software Testing
Factors that drive the adoption of test automation in 2020
- Ability to strategize and plan.
- Finding resources and skills to make use of automation tools and frameworks.
- Ability to assess related risks.
- Exploration of tools.
- Providing instant feedback so that developers can gain insights into various areas that require improvement.
- Conducting multiple tests throughout various phases.
- Increasing efficiency to reduce risks.
- Reducing defect fixing time and effort.
- Investing more time for the development and enhancement of features.
- Analyzing strategies to capture data to assess various releases.
- Ensure API testing to determine the functionality, performance, security, as well as reliability of the application tested for.
Automation testing trends to watch out in 2020
1. Test automation to improve quality for Agile and DevOps
As businesses face constant pressure to meet the changing customer demands and expectations, agility becomes the key to success for coping with the transformation. Additionally, enterprises lookout for agile processes and software delivery approaches such as DevOps to ensure a faster delivery time of applications to the market. Agile-DevOps transformation has brought about higher levels of quality, flexibility, efficiency, and productivity to achieve faster release cycles, improved quality, and increased ROI.
Related Reading: Myths About Load Testing in Agile Environment
2. Usability testing to ensure improved user experience
Mobile and eCommerce applications are great enablers for businesses today. User Interface, as well as the operational flows, play a critical role in determining whether the visitors continue to be users or not. A quick application loading time can enhance your brand loyalty and improve customer experience and retention.
3. Big Data testing to tackle huge volumes of data
Historical data helps enterprises gain critical insights on future plans and objectives and helps them to be proactive through predictive maintenance, machine learning, and AI techniques. Mining structured, as well as unstructured data, are hence important for effective testing.
4. IoT testing for well-connected devices
According to the latest reports published by Statista, the entire installed base of the Internet of Things connected devices is forecasted to amount to 75.44 billion worldwide by 2025. This figure was just 6.4 billion during the year 2016. This increase in the figures illustrates the massive requirement for effective IoT strategies in test automation. This testing includes testing of OS, various software and hardware of the IoT connected devices, different communication protocols, and so on.
5. AI and ML testing for technological innovations
The global AI software market is forecasted to grow to $14.7 billion by the year 2025. AI and ML technologies such as gesture recognition and speech recognition are taking over the world by storm. Similarly, technologies such as neural networks and predictive maintenance also require high-quality testing methodologies for technological innovations.
6. Blockchain Testing
Blockchain Testing enables smart contracts and prevents fraudulent transactions, especially when dealing with digital currencies such as Bitcoin. Blockchain debugging is thus crucial for enabling streamlined and smooth financial currencies.
7. Cyber Security Testing
Cyber vulnerabilities increase day by day and tackling them requires effective testing practices. (CEH) Certified Ethical Hackers with key security tools and technologies can safeguard applications from malfunctions and cyber attacks.
8. RPA Testing
RPA (Robotic Process Automation) testing ensures enhanced output, facilitates high-end performance and reduces the efforts required while performing end-to-end testing. For instance, robots can enhance a workspace with its capability to perform redundant tasks. RPA testing is thus crucial for effective applications with faster development cycles.
Related Reading: Quality Assurance in Software Testing – Past, Present & Future
Integrating automation testing into your workflow requires you to take into account how it will impact the people, process, and technology of your organization. It’s also crucial to measure if automation fits into the cycle of continuous integration and delivery and how it will merge with your software development lifecycle. Fingent helps you reap the benefits of test automation. Contact us to know more.
Testing Types And Strategies: Choosing A Testing Method
Understanding the basics of software testing is crucial for developers and quality assurance specialists equally. To deploy a better software and to find bugs that affect application development, it is important to learn about the different types of software testing.
Types Of Software Testing
Testing is a process of executing a software program to find errors in the application being developed. Testing is critical for deploying error-free software programs. Each type of testing has its advantages and benefits. Software testing is broadly categorized into two types; Functional and Non-Functional testing.
Functional Testing Versus Non-Functional Testing
Functional Testing is used to verify the functions of a software application according to the requirements specification. Functional testing mainly involves black box testing and does not depend on the source code of the application.
Functional Testing involves checking User Interface, Database, APIs, Client/Server applications as well as security and functionality of the software under test. Functional testing can be done either manually or by making use of automation.
The various types of Functional Testing include the following:
- Unit Testing
- Integration Testing
- System Testing
- Sanity Testing
- Smoke Testing
- Interface Testing
- Regression Testing
- Beta/Acceptance Testing
Non-Functional Testing is done to check the non-functional aspects such as performance, usability, reliability, and so on of the application under test.
The various types of Non-Functional Testing include the following:
- Performance Testing
- Load Testing
- Stress Testing
- Volume Testing
- Security Testing
- Compatibility Testing
- Install Testing
- Recovery Testing
- Reliability Testing
- Usability Testing
- Compliance Testing
- Localization Testing
The 7 Most Common Types Of Software Testing
Type 1: Black-box Testing
Black-box testing is applied to verify the functionality of the software by just focusing on the various inputs and outputs of the application rather than going deep into its internal structure, design, or implementation. Black-box testing is performed from the user’s perspective.
Type 2: White-Box Testing
The White-Box software testing strategy tests an application with access to the actual source code as well as focusing on the internal structure, design, and implementation. This testing method is known by different names such as Open Box testing, Clear Box Testing, Glass Box Testing, Transparent Box Testing, Code-Based Testing, and Structural Testing. White-box testing offers the advantage of rapid problem and bug spotting.
Type 3: Acceptance Testing
Acceptance Testing is a QA (Quality Assurance) process that determines to what extent a software attains the end user’s approval. Also known as UAT (User Acceptance Testing) or system testing, it can be testing the usability or the functionality of the system or even both. Depending on the enterprise, acceptance testing can take the form of either end-user testing, beta testing, application testing, or field testing. The advantage of acceptance testing is that usability issues can be discovered and fixed at an early stage.
Related Reading: Quality Assurance in Software Testing – Past, Present & Future
Type 4: Automated Testing
Automated testing is a method in which specialized tools are utilized to control the execution of various tests and the verification of the results is automated. This type of testing compares the actual results against the expected results. The advantage of automated testing is that it avoids the need for running through test cases manually, which is both tedious and error-prone, especially while working in an agile environment.
Type 5: Regression Testing
Regression testing is a testing practice that verifies whether the system is still working fine, even after incremental development in the application. Most automated tests performed are regression tests. It ensures that any change in the source code does not have any adverse effects on the application.
Type 6: Functional Testing
Functional Testing tests for the actual functionality of the software. This type of testing focuses on the results of the system processing and not on how the processing takes place. During functional testing, the internal structure of the system is not known to the tester.
Type 7: Exploratory Testing
As the name indicates, Exploratory testing is all about exploring the application where the tester is constantly on the lookout for what and where to test. This approach is applied in cases where there is no or poor documentation and when there is limited time left for the testing process to be completed.
Related Reading: A Winning Mobile Testing Strategy: The Way to Go
All the methods mentioned above are only some of the most common options of software testing. The list is huge and specific methods are adopted by development vendors based on the project requirements. Sometimes, the terminologies used by each organization to define a testing method also differ from one another. However, the concept remains the same. Depending on the project requirement and scope variations, the testing type, processes, and implementation strategies keep changing.
Like to know more about Fingent’s expertise in custom software development and testing? Get in touch with our expert.
There have been major changes in the way software and applications are built in software companies. Enterprises have moved beyond the conventional waterfall development model to more flexible Agile development environments. The whole idea of agile development methodology is to manage change incrementally and effectively in software and external functionality, and to speed up the development of quality software. And a major challenge here is to provide quick feedback on the change introduced in the software and the impact of it. The cornerstones of a proper agile approach to software development are early testing, rapid feedback, and effective communication.
Load testing in agile environment
Load testing is feeding the system with largest/ toughest tasks it can operate with, to see if the system can endure the stress. Historically, in waterfall development model, load testing gets pushed to the end of the project, assuming that only minor tweaks will be required to meet the performance criteria. With agile, load testing should be carried out often, and incrementally. But due to lack of adequate, trained and dedicated testing resources, lack of flexibility of load testing tools, lack of proper criteria, or due to infrastructure limitations or the common misconceptions among people around what’s possible with load testing and performance testing, these often get pushed to the end of agile development process.
Unless employees get beyond the following testing myths and delve deeper into the process, and realize what all is possible with today’s performance testing capabilities, we can’t do justice to the fully agile concept. So, here are the common misconceptions that employees should get rid of, to drive their testing initiatives deeper into the agile environment.
Myth #1: Load testing is all about breaking the system, so, unless the system is fully developed, load testing can’t be attempted.
Load testing is like a full-throttle, threshold limit or peak-usage stress test. Thus, in a way, people think of it as forcing the entire system to its breaking point. So, unless the entire system is built, how can you break it? Naturally load testing can’t be fitted into the small, iterative batch of Agile; thinks most of them.
You can help dispel this myth by creating a suite of modular performance test scenarios for common queries and crucial transactions. Performance test can be carried out in individual modules as easily as functional tests are done. Performance tests should work alongside functional tests with every sprint. This will prove that there’s lot more to performance testing than its objective mentioned previously, and will unveil the different ways it can fit into the usual Dev-Test-Ops cycle.
Myth #2: It’s risky
Load testing might sound risky, but, it is always safe to run a controlled test than to let the system fail under scenarios which were easily preventable and thereby avoid putting your reputation, revenue and users in jeopardy because of it.
Myth #3: It takes a long time in Agile
This can be a valid excuse if you push the load testing towards the end of the project, but in agile environment, the process of creating and executing the test at scale needn’t be that complex and time consuming. Think about the requirements upfront and put performance SLAs (Service Level Agreements) on the task board. This will help to automate basic testing processes. In fact small, iterative tests with incremental objectives allow testing in equal or much less time.
Myth #4: Developers needn’t concentrate on Performance until Functionality is complete
One of the key ideas of Agile programming is to find issues early and fix them fast and at lower costs, which is why we find a lot of testers learning to code, and adopting trends like automated tests and test-driven programming. This helps them run tests alongside development. The same is expected from load testing. Isn’t it always better to find errors or issues right when the code is written than to skim through the entire program to find a tiny line of problematic code at the end of development? Many back end interfaces like, Web services or SOAP open up opportunities for testing performance along various application paths even before the app functionality is completed.
Myth #5: Load Testing Doesn’t Involve the Whole Team like in Functional Testing
Earlier, we had a single individual or a group dedicated to conduct specific tests like the performance testing, but in an agile environment, everyone is a Tester (or developer) and all members contribute in every phase of software development to achieve the quality end product. Though there are testing experts and QA specialists, developers are still made to write their own test cases and similarly, operation specialists can identify issues and work to fix them.
Myth #6: Load testing is unnecessary if we do thorough testing in lab
Testing in a lab environment is good for testing the app, but it doesn’t cover the broader infrastructure. Many a times, we have seen serious, unexpected issues cropping up outside of the apps. Load testing in production can’t replace lab testing, but it reveals issues that other tests can’t, like, Database issues, Network bandwidth issues, unbalanced web servers, App server issues, DNS routing problems and Firewall capacity issues.
Myth number #7: The right load test tool will do everything for me
You can’t trust on tools to do every task perfectly, however smart the tool may be. You will have to do certain basic tasks yourselves like, setting up meaningful KPIs, realistic master data, creating valid, repeatable test cases, realistic master data, analyze the results, etc. Eliminating the human involvement completely during the test might reduce the confidence in the code you deliver.
Myth number #8: Load testing is too manual
From the previous point, it’s sure that you have tools to automate certain aspects of Load testing, so that the test isn’t completely manual. There are so many ways you can automate load testing, it’s just about choosing the right processes to automate.
Load testing can reveal functional problems in the code that can’t be otherwise detected using single-user tests. However, unless you make load testing an integrated part of the development process, you can’t completely say that you have an agile development methodology, nor can you extract its benefits.