Introduction – The AI that we have known
The course of action that artificial intelligence brings to today’s human activities is inevitable. No one can argue anymore about the impact of artificial intelligence. Everyone refers to AI as a new labor force, its power has occupied a notable market share in the labor market, especially in the information technology labor market. Artificial intelligence with advantages such as efficiency, automation, convenience in simple and available operations. However, current comments about artificial intelligence still do not ensure specificity, as artificial intelligence is a field, while its applicability in other fields is a scope.
From being recognized as the most supportive factor in IoT era
Therefore, the applicability of artificial intelligence is considered by other fields as a form of tool, also known as AI-powered tools. These tools are designed on a technical, scientific and technological basis embedded with artificial intelligence, thereby contributing to ensuring breakthrough efficiency and productivity compared to before. When approaching the problem in this way, we will clearly see the key role, hinge and opportunity that artificial intelligence brings to the job market rather than threatening it as we are afraid.
To become a potential threat to Techeads’ employment
Although the discourse that considers AI as a replacement for humans or a certain segment of the job market is completely grounded, it is based on society, specifically human resources. More generally, AI has accelerated the process of restructuring human resources based on resource allocation, making this activity instead occur in cycles of 1 to 2 decades on a small scale, according to each organization, now taking place urgently and on an industry scale. Previously, the main responsibilities of a job position would be assigned to each individual holding that position, modern human resource management theory has attempted to separate responsibilities and individuals into two independent entities.
However, that process is almost contrary to social psychology as people are social, employment relationships are almost inseparable from human relationships. The emergence and expansion of AI is like a push, this push is so strong that it suddenly makes us feel like AI has threatened human jobs, instead, rather, AI has accelerated the process.
AI testing tools bring many benefits to software testing engineers, helping to improve testing efficiency, accuracy and productivity. Even so, it is also important to be aware of challenges related to data, lack of understanding and unemployment risks. Software testing engineers need to adapt to this change by cultivating knowledge about AI, improving data analysis skills and developing critical thinking to work effectively with AI tools, contributing to ensure the best software quality.
Nevertheless, the above mentioned thing came to an end so quickly that we have to find a reason to protect the stability of this job market.
The challenges of AI
The dual nature of AI are both a promising tool and a source of challenges in the field of software testing. It hints at the optimistic views many hold about AI, while also acknowledging the significant hurdles that need to be addressed.
People seldomly aware of the challenges of such discourse being discussed, due to rather optimistic than rational explanation as these following features that AI known for:
- Data dependency: The effectiveness of an AI tool depends on the quality and reliability of the training data. Wrong or incomplete data can lead to inaccurate testing results, affecting software quality.
- Lack of understanding: AI is a “black box” for many test engineers, making it difficult for them to understand how it works and the reasons behind the results it produces. This can lead to a lack of trust in AI tools and limit its adoption.
- Risk of unemployment: Some fear that AI could completely automate software testing, leading to job losses for test engineers. However, this is unlikely to happen in the near future because AI has not yet been able to completely replace human critical thinking and creative thinking abilities.
Reallocating manual testing
Manual testing is basically a combination of operations to identify potential errors during user experience and interaction. These operations are performed by technicians with expertise and experience, using the necessary tools to complete the job. This understanding of manual testing belongs to the nature of the activity. White-box testing or black-box testing both revolve around human operations and this is an extremely important input for artificial intelligence to have a basis to continue saving labor power during work performance instead of It is a common misconception that artificial intelligence will replace manpower in the execution of work.
Together with AI-powered tools, the work of manual testers will be significantly enhanced in two aspects:
- (i) Versatility: manual testers can uncover a wide range of issues, including usability problems, logical errors, and inconsistencies that might be underestimated by automated tools.
- (ii) Exploration: testers can now perform exploratory testing, where they dig into the application without a predefined script, potentially discovering unexpected issues.
Some useful AI tools to support the manual testing
To name but a few, there are several famous AI-powered tools being used in manual testing at the moment.
- Applitools:
known as an AI-powered automated user interface (UI) testing tool that helps ensure interface consistency and accuracy across browsers and devices. The highlight of Applitools is its ability to use computer vision technology to compare interface screenshots, automatically detect changes, and report errors visually. As a result, test engineers can save time and effort in manual interface testing, while improving the accuracy and efficiency of the testing process. - TestCraft:
known as a comprehensive software testing support platform, integrating artificial intelligence (AI) to optimize the testing process. This tool provides features such as:- (i) Test data analysis: TestCraft uses AI to analyze historical data about errors and test results, thereby predicting potential error areas and recommending suitable test cases.
- (ii) Intelligent Automation: TestCraft can automate repetitive testing tasks, freeing up test engineers’ time to focus on more complex tasks.
- (iii) Intelligent reporting: TestCraft provides detailed reports on test results, including analysis of defect trends and factors affecting software quality.
- Testim.io:
known as a cloud software testing platform that uses AI to create automated test scenarios quickly and easily. This tool supports testing of many types of applications, including web, mobile, and API. The highlight of Testim.io is its ability to learn from user behavior during testing, automate iterations, and continuously improve the accuracy of test scenarios. - Applause:
known as a crowd testing platform that combines the power of a global tester community with artificial intelligence (AI) to perform software testing efficiently. The platform uses AI to assign the right testing tasks to testers with the right expertise, while also automating management processes and reporting test results. Thanks to that, Applause helps businesses save costs and testing time, while ensuring software quality is thoroughly tested by a team of experienced testers.
Conclusion – AI is to become the most effective booster
To conclude, it can be seen that the support of artificial intelligence in all areas of manual testing is inevitable as described. Some popular AI-powered tools today such as Applitools, Selenium, Testuff,… are favored by many testers, depending on their working style, projects and habits. AI should be understood as a force that comes in handy and under control, not any threatening as some people assumes it to be. AI is domain itself with both macro and micro scope in every field. The application of AI in manual testing will bring unprecedented benefits to testers with increased work efficiency per labor unit, work performance growth also by labor unit leads to reducing headcount is not any concern but very plausible.
References:
- Moving business forward with artificial intelligence | NashTech (nashtechglobal.com)
- Image from Global Technology Consulting Services | NashTech (nashtechglobal.com)
- https://applitools.com/
- https://home.testcraft.app/
- https://www.testim.io/
- https://www.applause.com/