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.
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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.
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Developers and Testers: Two people from two different planets working together to deliver a quality product to an “Alien”! Funny isn’t it?! The story is more or less the same in every software firm. A developer works hard to develop a product, which he handles with so much care and gentleness; a tester works hard to break this code handling it in the worst possible cases and scenarios to test its defects, resistance power, and strength.
So, when a developer finally hands over his much-nurtured sprint to the merciless team of testers, ready to execute the ‘out of the box’ testing, this is what happens:
o User Interface element alignment? Off!
o Inputting special characters instead of string? Unknown result!
o Putting 01 for a range from 1-10? Crash!
o Entering a date in the past? Crash!
o Working with special characters in names like, Neil ’O? Nope!
And there they silently demolish the code….
Sandpapers scrape and scratch, but to create polished surfaces!
So, there is this formidable wall between developers and testers most of the time, which stops them from having a sustained smooth association. There are many reasons attributed to this silent difference in opinions. For instance, developers are often seen to possess parental attachment to the stuff they create. It’s more like; “I know my kid, he wouldn’t do that”, when the reality is, the kid actually did just that! It might sound silly, but a good programmer should learn to be more objective and accept the fact that they have to do terrible things to their code. Because, if they don’t, others will!
Another problem is, developers have little or no idea how their code is going to be handled at the other end of the boundary wall, and thus they aren’t ready for the million bugs that testers backfire at them. Being in the developing environment, which usually revolves around positive scenarios of how to make things work efficiently, they often lack the ability to switch to ‘what can go wrong’ mind state.
Programmers are pros at breaking down complex scenarios into small programmable chunks; ‘The Computers’! – What can be a better example to support that statement! Can you imagine, the computers do all their work with just 0 & 1, the binary numbers and some operations on these, like OR, AND, NOR, NAND, NOT, XOR and XNOR! On the other hand, testers are experts in finding complex scenarios, where they can probably uncover a glitch to break the system!
Thus, the million justifications, silent cursing, and muted murmurs before finally reaching “The end product”! Though these are the situations, the product keeps gaining value from the combined efforts of programmers and testers. And that is how self-resistant codes are born; after all, testers care for their end product, you see! And it’s for developers to realize how their application can flunk in million ways, any time!
Developers with Testers- the perfect wedding!
Though organizations often make efforts to improve the communication between developers and testers, this is often not enough. If only their thoughts cross these barriers and start thinking somewhat within the same boundaries, can we expect super conflict-free Apps! (Pun intended)
It’s always a good idea for programmers to sit through some training/seminars that test engineers attend. A Developer who has attended such seminars is found to avoid making silly (yet common) errors and is often more vigilant. Moreover, a programmer’s awareness of testing tools, methods and processes goes a long way in enabling smooth and fast testing practices. He learns to stand in tester’s shoes while writing codes, to understand what in his code can probably be a tester’s target, what changes he makes will give testers a tough time and what makes it easy, thus making the entire process productive. For instance, any coder might not find it an issue to change a button label from, say, “Clear” to Reset”, but a developer who has sat through the testers’ seminar/ training can probably understand why this silly button label change affects the testing and how it can be frustrating to his colleagues in Testing.
The developer can thus create applications keeping in mind the worst cases that his code can go through, or gets tested in. He will eventually learn to make discipline around testing (or breaking) his own creation- his own code, which indeed makes him a better programmer. So an out of the box thinking is always good! Also, testers need to understand that everything changes with time, and so are the cases with software development too. It doesn’t make sense to change/ rebuild the code every time there’s a change, but the efficient solution is to encourage programmers to make wise changes in their code than to frequently change it.
One team- One goal
In hybrid agile development environment like ours, we handpick Engineers from departments, to form a dedicated team working together throughout the lifecycle of a particular project, with the single goal- the final release! I say ‘Engineers’ and not testers, designers or Business Analysts (BAs), because everyone in this team is a developer, and contributes to the development. This ensures that there are no separate groups (like developing group, testing group, BA, etc.) coming into play at various stages of developing a product, but a single team dedicated to every phase of the product development- from day 1, Sprint 0 to the Release of the End Product! This practice guarantees that the resources and their time are fully and efficiently utilized at every phase.
For instance, with the beginning of a new project, from day one, that is, the initial product backlog creation stage, you have a team in place, consisting of testers, QA experts, developers, UI designers, DBAs and analysts gathering at every SCRUM meetings, to discuss and contribute to everything they may not be directly involved in. Even before the development of the product starts, that is, at the programming estimation phase, testers start writing test cases, estimations etc. for features that will be developed, BAs do the requirement analysis and stuff, Developers plan and decide on features, and so on. At each SCRUM meeting, the team determines how it shall accomplish the work that needs to be done. This way, not only everyone has tasks throughout the sprint, but also knows and understands the roles, responsibilities, hard work and effort taken by each member. Thus, they learn to respect, understand and get on well together to work as a REAL POWERFUL TEAM!
Ultimately, we know the entire team has one single goal- “A Quality product”! It’s just that the developers and testers take entirely different paths to get there, just as in the much-admired Love stories!