Though it may first appear just an easy niche for software developers, software testing is an expansive subject that holds within it a lot of importance and value as it determines the quality aspect of the product. This is why the proper use of technology is as crucial in testing phase as it is during software development phase.
It is also undeniable that both preventive and proactive software test efforts require a versatile set of skills, technical specialization and project-specific expertise. The way software testers use their potential in building and executing the testing strategy ultimately contributes to delivering a polished software for end users.
On the other hand, Artificial intelligence and machine learning technologies have also begun to disrupt the testing space, and testers with a receptive mind and focus on user experience are likely to reap great opportunities from AI-enabled software testing.
However, the need for AI raises intriguing questions, one of which is: Has human potential been used well in software testing so far?
Based on the present scenario, it is evident that software testing domain seems to be deprived to an extent of reliable, organized and pragmatic human endeavors needed to drive fruitful process.
The following piece poses as an attempt to find the answer as to why human efforts have not been properly brought to testing.
The role of human skills and accuracy in software testing
Software testing skills are not just limited to checking the functioning of different features and elimination of bugs using various use cases, but it is more about determining the impact of user experience and user behavior on overall product satisfaction. Testers ensure that product remain bug-free and inspires high usability. Considering all the comprehensive aspects of testing, the role of human skills and accuracy expands to include maximum scenarios with success and failures.
Lets take an examples of how testers can masquerade to be a user who has little knowledge of issues that are likely to occur on a transaction page they visit.
While using an ecommerce mobile app, they are stuck on the page where they have entered all the required payment details in the fields. But they still encountered a message saying, ‘We are unable to process your request at the moment due to an unexpected problem.’
There is a possibility that the message may persist even after making another attempt, which will encourage users to quit the app. This scenario may represent the dark failure of Positive Testing, which demands an accurate retest with Negative Testing. The test determines that neither the input is invalid nor has the user performed any wrong action, revealing that there might a problem of system cookies.
Testers playing users is the necessity
The above examples simply talks about how testers must religiously connect with users long before the application is downloaded and explored. The test requirement for such specific scenario often go omitted from the strategy, which indicates the lack of human wisdom for real-time use cases. This is where, to fill the gap, contemporary testing methods such as regression testing, exploratory testing, and usability testing come into picture.
Many times software testing experts do not heed at how things might appear from user’s perspective when they actually begin to use the product, which results in painful loss of customers. User’s inability to perform the transaction surely manifests tester’s limited eye and adds to frustration. To improve this experience, you can choose to add the reason in a convincing way, probably with effective steps to clear the error.
There is a good number of mobile apps that still do not establish clarity around this transactional issues, which further goes to explain how human potential has not been properly invested for impeccable testing.
Absence of lateral thinking and in-depth scrutiny
It is not rate to hear the term out-of-the-box thinking especially in creative design practices. Like software design, software testing is where unique thought process is expected. Applying lateral thinking to different aspects of testing definitely helps achieve better results. However, unfortunately, not all software testing companies have professionals dedicated to in-depth analysis and outstanding brainwork.
Due to time constraints or any other solid reason, they tend to prefer Black box testing and Sanity testing methods without peering much into the actual internal structure or working of the functionality being tested.
Such surface level methods adopted by QA engineer focuses mainly on whether or not all the menus and commands in the product work healthily, which omits many other minute performance checks.
Prejudice-driven skill recruitment and choices
Software testing professionals and aspirants go in the wrong direction the moment they rely on someone else’s shallow word-of-mouth recommendations that may or may not be worth consideration. One of the reasons why human efforts have not been properly harnessed in the testing industry is short-sighted choices of technical skills and inconsiderate hiring process. Being in the industry that revolves around constant transformations and rapid advancements, we need to expect fresh, invigorating and highly updated insight and experience.
Instead, the testing professionals fall short of being genius and end up selecting the archaic wisdom that has been there for long. This further suffocates the passion for rich learning and limits all the ways the human power can be squeezed effectively.
Will the touch of automation instill hope for software testers?
Though there has been a fierce thrill about Artificial Intelligence coming to transform software testing stage, it is still not specific as to what areas the AI implementation may improve. Of course, the first thought that comes to anyone’s mind when uttering AI is an idea of automation.
However, as Pradeep Soundararajan would like to describe in his recent interview with Tricentis, we are still in the dawn of AI-powered solutions. In his words, ‘We’re still trying to think about where can we fit in AI, or does AI really fit in?’
Pradeep, while emphasizing on AI-driven Testing, also explains how in future we might also be able to use AI for exploratory testing whose purpose would be to not just ‘aid’ but ‘drive’ functional tests. The idea of aiding testers – which is not merely automation – sounds as intriguing as it is fantastical.
The software testing industry is still striving to innovate the system where human professionals are struggling to achieve unforeseen efficiency and flawless outcome. Even after performing thorough test process, the final product still lacks certain quality that end users can approve. It is the after implementing the software solution that real issues begin to surface, which directly implies that testing professionals need to fill the missing element that can help entrench an unblemished product testing strategy in place. Surprisingly, adoption of AI and machine learning might raise hope for testers to fill this unexpected gap in future.