How to choose a laptop for work in 2025: important parameters

The IT industry continues to actively develop, creating thousands of job openings for specialists in various fields. Among the in-demand professions, QA Engineer remains. This specialist is responsible for ensuring the quality of digital products, preventing errors, and identifying vulnerabilities before release to the market.

But is it worth studying to become a tester in 2025 when technologies are becoming automated, and competition in the job market is increasing? What prospects await newcomers? The answer to this question requires a detailed analysis of the market, demand for specialists, salary levels, and future trends.

Demand for the profession: Is it worth studying to become a tester in 2025

IT companies are paying increasing attention to the quality of products, making testing a critically important stage of development. According to the Statista report, by 2025, global spending on software quality control will exceed $60 billion, indicating a growing need for qualified engineers.

The labor market also confirms this trend:

1. USA — QA job openings have increased by 11% over the past two years (Glassdoor data).
2. Europe — demand for testing specialists has increased by 15% in 2023-2024 (Eurostat study).
3. Russia and CIS — on hh.ru and LinkedIn, the number of open positions in the niche is around 8,000 vacancies, with an average salary ranging from 100,000 to 250,000 rubles depending on qualifications.

Despite the development of automated testing, it is impossible to completely replace manual methods. Hybrid approaches (combined checks) require professionals to have skills in analysis and knowledge of programming.

Skills and competencies in demand in 2025

To understand whether it is worth studying to become a tester in 2025, it is necessary to consider the skills that will be in demand by employers. Technological advancements lead to increased requirements for QA specialists.

Basic competencies:

1. Testing methodologies — understanding SDLC, STLC, test design (Boundary Value Analysis, Equivalence Partitioning, etc.).
2. Working with bug tracking systems — JIRA, TestRail, Redmine.
3. Basic programming knowledge — Python, Java, JavaScript.
4. Working with SQL — queries for database testing.

Advanced skills for competitiveness:

1. Automated testing — Selenium, Appium, Cypress.
2. API testing — Postman, REST-assured.
3. Containerization and CI/CD — Docker, Jenkins, GitLab CI.
4. Software security — OWASP, vulnerability testing.

Relevant programming languages

Programming knowledge is becoming an important requirement for testers. In 2025, preference is given to languages such as:

1. Python — the best choice for test automation due to its simplicity and powerful libraries.
2. Java — used in large projects, especially with Selenium.
3. JavaScript — applied in frontend testing with Cypress and Playwright.
4. C# — used in corporate products (Microsoft, Unity).

How much do testers earn in 2025

Before deciding whether to study to become a tester in 2025, it is important to study the data on specialists’ salaries. The amounts depend on experience, level of qualification, and region of work. Current data:

1. USA: $80,000 — $120,000 per year.
2. Europe: €40,000 — €90,000 per year.
3. Russia and CIS: 100,000 — 250,000 rubles per month.
4. Asia: $30,000 — $70,000 per year (depending on the country).

Manual testers earn on average 30% less than automated testing engineers. For example, in Russia, a Junior QA Manual earns 100,000 rubles, while an automator with the same level of knowledge earns 140,000 rubles.

Pros and cons of the tester profession

Analyzing the advantages and disadvantages of working in the testing field allows for an objective assessment of the career choice’s prospects. The level of competition, growth prospects, and job nature determine how comfortable it is to work in this area.

Career Pros: Is it worth studying to become a tester in 2025

Testing engineers get the opportunity to quickly enter the field of information technology without a deep technical education. The entry threshold remains relatively low: mastering the basics takes from four to six months, making this profession accessible to beginners. Companies are interested in specialists capable of quickly identifying errors, testing software functionality, and ensuring its quality. The demand for testers in the market remains stable because without quality control, it is impossible to release a digital product that meets user requirements.

Career prospects open up opportunities for transitioning to automated testing, DevOps, programming, or project management. Developing skills in related areas increases income levels and expands professional opportunities.

Cons of working as a tester

High competition among newcomers creates challenges at the start of a career. The number of vacancies for Junior QA is limited, and the requirements for candidates are constantly increasing. Having basic knowledge does not always guarantee employment, so novice specialists have to additionally study automated testing and work on creating a portfolio.

The nature of the work involves performing repetitive tasks. The process requires attentiveness and meticulousness but includes many routine operations, which can reduce motivation. Automation partially solves this problem, but without learning programming languages and mastering specialized tools, it will be difficult to do so.

Where to study to become a tester in 2025

Online courses, university programs, and internships are available for mastering the profession.

Online courses:

1. Coursera (Software Testing and Automation).
2. Udemy (Complete Software Testing Bootcamp).
3. Skillbox, Otus, Netology (Russian-language courses).

Certifications:

1. ISTQB Foundation Level (international standard).
2. Certified Selenium Automation Tester (automated testing).

Practice and internships:

1. Google, Microsoft, Yandex, EPAM offer programs for beginner QAs.
2. Open-source projects (GitHub) allow you to build a portfolio.

Is it worth studying to become a tester in 2025: conclusions

The growing software market, the need to test new technologies (AI, cloud services, blockchain), and the high demand for QA specialists make testing a good career opportunity. However, success in the profession requires continuous development, learning automation, and working with modern tools. Newcomers need to consider competition and choose courses that provide practical experience and a real portfolio.

The answer to the question of whether it is worth studying to become a tester in 2025 is yes if you are ready to master new technologies and develop professional skills.

Bugs are no longer hidden behind the backs of testers. Today, they are not caught by scripts or manual clickers – the stage has been taken over by AI in software testing. It doesn’t just check – it predicts, learns, analyzes relationships, catches logic failures before they make it to production. In the conditions of accelerated development and the CI/CD approach, it is the one who doesn’t just view bugs – but prevents them. While traditional methods drown in routine, artificial intelligence is rewriting the rules of the game. No noise or magic – just a clear algorithm, numbers, and results.

How AI Has Changed the Game in Software Testing

Standard checks are no longer coping with the scale of modern releases. Scenarios grow like yeast dough on a hot battery, and bugs escape even from experienced QA engineers. AI in software testing has eliminated this contradiction by combining scalability and depth of analysis.

Technologies no longer just automate. They learn, adapt, extract data from behavior patterns, process logs faster than a human can open a browser. Artificial intelligence has reshaped software testing not only in methodology but also in philosophy: from control to prediction, from manual routine to proactive quality.

Functionality of AI in Software Testing

AI analyzes error codes, identifies anomalies, builds defect models. The method of “check everything in a row” has been replaced by “check only what’s important.” Instead of Excel reports, there is real-time analytics and visual dashboards.

The working mechanisms include:

• machine learning on previous scenarios;
• auto-test generation based on commit history;
• risk determination based on system behavior;
• priority adjustment based on failure frequency;
• bug prediction based on code metrics and API interactions.

For example, integrating AI into the testing of large e-commerce platforms has resulted in a 36% decrease in defects in production over six months. This is the result of early detection of deviations, even before the first user click.

Top Tools

AI in software testing is implemented through a variety of solutions. However, not every tool is equally useful. Leaders stand out for their adaptability, customization flexibility, and scalability in a DevOps environment.

List of top tools:

1. TestRigor. Uses text commands instead of code. Increases diagnostic accessibility, reduces the entry threshold. Suitable for quick scenario generation, especially in Agile conditions.
2. Parasoft. Combines AI algorithms with API tests. Expands coverage, automates log analysis, reduces the tester’s workload. Supports regression testing with machine learning.
3. Roost.ai. Focuses on dynamically allocating resources for each test. Eliminates environmental influence, speeds up the QA cycle, ensures independence from configurations.
4. Cucumber. Supports BDD approach. Works in tandem with neural networks, speeds up logic error detection.
5. LambdaTest. Provides a cloud environment for tests in different browsers. Integrates AI for real-time bug analysis, simplifies cross-platform checks.
6. Selenium (in conjunction with AI). Expands the capabilities of classic Selenium through neural network modules. Predicts element failures, optimizes locators.

Each of these solutions enhances QA efficiency, but only in the context of a sound strategy. Without an architectural approach, even the best tools lose their effectiveness.

How AI Automation Handles Bugs

AI in software testing does not just detect defects. It interprets system behavior, identifies causal relationships, and prioritizes tasks. Automation has ceased to be mechanical repetition: it evaluates, learns, adapts models to the application’s specifics.

This reduces the proportion of false positives, speeds up the CI/CD cycle, and minimizes the risks of missed bugs. The implementation of AI modules in a large HR platform reduced the number of unnoticed defects in the release by 44% in 3 months.

How AI Works Remotely in Software Testing

Cloud solutions have strengthened the influence of artificial intelligence in software testing. QA engineers gain access to environments, tools, and analytics regardless of geography. Remote work is synchronized in real-time, and test logic adapts to user behavior.

Roost.ai and LambdaTest allow running tests online, simultaneously capturing logs and predicting failures based on heat maps of interactions. Online architecture integrates AI, reduces infrastructure load, and accelerates scalability.

Unconventional Tester Hacks

AI in software testing provides an advantage only if the engineer knows how to direct it. Efficiency increases when principles of adaptive model learning, correct data labeling, and risk zone metric construction are followed.

Practical techniques:

• train the neural network only on validated scenarios;
• avoid overfitting on unstable features;
• evaluate performance based on real failure metrics;
• isolate environmental fluctuations from analysis logic;
• use custom bug prioritization logics based on impact levels.

A skilled tester turns AI into an ally, not an unjustified tech burden. Otherwise, even a powerful model will not solve software quality tasks.

Implementation Risks: Where AI Makes Mistakes

AI in software testing, despite its high potential, is not immune to risks. Algorithms often fail in unstable architectures, variable environments, and a lack of training data.

Common risks:

• overfitting on incorrect patterns;
• excessive trust in auto-generation without review;
• replacing engineering thinking with a “magic button”;
• false positives in unstable data.

In one fintech company, an AI module mistakenly missed a defect in the interest calculation algorithm. The reason was the lack of analogs in the training dataset. Therefore, critical scenarios require manual verification, not blind trust in AI solutions.

Changes in Software Quality

AI in software testing has rebuilt the foundation of program verification. Already, there is a growing demand for QA specialists with ML and automation skills. The trend is intensifying: by 2027, according to Gartner’s forecast, up to 80% of regression tests will transition to AI architecture.

Artificial intelligence speeds up releases, reduces defect elimination costs, and minimizes the human factor. However, effectiveness depends on a systematic approach and proper integration. Machine learning enhances but does not replace thinking. That’s why flexible management skills with these tools become mandatory for a QA specialist.

Conclusion

AI in software testing has created not just a technology but a tool for competitive advantage. Release speed, product stability, cost reduction – everything hinges on the efficiency of integrating digital intelligence. Only in the hands of an expert does it reveal its real potential, minimize risks, and change the approach to software quality.