
Modern Software Testing Life Cycle

Software testing is my passion
Super excited to be speaking this Friday, 18th April 2025 on a topic that’s close to my heart:
“AI-Powered Testing for the Next Generation of Software”
In this session, I’ll dive into how AI is transforming software quality assurance—from test case generation and self-healing automation to intelligent defect prediction and more.
Let’s explore the future of QA together!
💬 Stay tuned and feel free to reach out if you’re curious about what’s coming next in the world of intelligent testing.
Given today’s digital economy, effective software is a crucial tool to boost business processes, productivity, and customer satisfaction. However, applications that were not properly tested can result in security vulnerabilities, systems breakdown, and losses. Therefore, software testing becomes indispensable to performance and reliability as it significantly mitigates risks. Deploying a structured testing procedure enables bugs to be detected early, thus preventing expensive failures post-deployment. Nobody wants to put their time and trust in software that is slow or unreliable, and e-commerce systems must be able to manage tremendous amounts of traffic during events like Black Friday. This means that robust stress testing is a requirement. Another area that requires attention, and which is more in the limelight recently, is routine security testing systems due to increasing cyber threats that need to comply with regulations such as GDPR or HIPAA. Such measures are needed to protect information and sensitive data to prevent losing user trust. Furthermore, automation testing reduces workload and long-term costs, allowing for better efficiency, and scalability of businesses. In the end, businesses need to understand that software testing goes beyond just didactic obligation and becomes of utmost importance from a strategic quality intervention especially in these technologically inclined eras to sharpen the competitive edge, mitigate risks, and promote user satisfaction more
In today’s fast-paced software development landscape, ensuring software quality is more critical than ever. Traditional testing methods, though essential, often struggle to keep pace with the complexity of modern applications. Enter AI-driven anomaly detection—a game-changer in Software Testing (ST) and Quality Assurance (QA).
What is AI-Driven Anomaly Detection?
AI-driven anomaly detection leverages machine learning (ML) and artificial intelligence (AI) to identify unexpected patterns, deviations, and defects in software testing and QA processes. Unlike traditional rule-based testing, AI continuously learns from historical data, system behaviors, and real-time test execution results to detect outliers, defects, and inefficiencies before they cause significant failures.
How AI is Revolutionizing Software Testing & QA
AI is transforming software testing and quality assurance in several ways:
1. Intelligent Test Case Optimization
AI analyzes test coverage, execution history, and defect trends to recommend the most effective test cases, eliminating redundant tests and enhancing efficiency.
2. Automated Defect Detection & Prediction
By learning from past defect data, AI can predict areas of the codebase most likely to contain bugs, allowing teams to focus their testing efforts more effectively.
3. Log Analysis & Failure Detection
AI-powered anomaly detection tools scan logs in real-time, identifying unusual behaviors, security vulnerabilities, or system failures before they escalate.
4. Self-Healing Test Automation
AI adapts automated tests dynamically when UI or functionality changes occur, reducing maintenance efforts and improving test reliability.
5. Performance & Load Testing Insights
Machine learning models detect anomalies in performance metrics, such as response times and resource usage, ensuring applications remain scalable and efficient.
Key Benefits of AI-Driven Anomaly Detection in ST & QA
Final Thoughts
AI-driven anomaly detection is not just an innovation, it’s a necessity for organizations aiming for higher efficiency, reliability, and quality in their software products. As AI continues to evolve, integrating it into software testing and QA processes will become a competitive advantage for businesses striving for excellence in the digital era.
Is your organization leveraging AI in its testing and QA strategy? Let’s discuss how AI can elevate your software quality!
In the fast-paced world of software development, ensuring the quality of a product is paramount. Software testing plays a crucial role in identifying defects, improving usability, and verifying the functionality of an application. However, within the field of software testing, two roles often cause confusion: Software Development Engineer in Test (SDET) and Quality Assurance (QA) Analyst. While both aim to deliver high-quality software, their approaches, skill sets, and responsibilities differ significantly. This article aims to clarify these differences and shed light on the impact each role has in modern software development.
A Quality Assurance Analyst (QA Analyst) focuses on ensuring that the product meets user expectations, functional requirements, and overall usability. They are primarily concerned with manual testing and exploratory testing, evaluating the product from the end user’s perspective.
– Manual Testing: QA Analysts execute test cases manually to identify defects and ensure that the software meets its functional requirements. Manual testing is essential when testing user interfaces, workflows, and usability aspects that are challenging to automate. – Test Case Design: They write and design detailed test cases based on requirements, ensuring comprehensive coverage of the application’s functionality. – Exploratory Testing: QA Analysts engage in unscripted, exploratory testing to uncover potential edge cases and usability issues that automated tests may not identify. – Collaboration with Teams: They work closely with product owners, developers, and designers to validate workflows and ensure the application is user-friendly. – Bug Reporting and Tracking: Defects found during testing are logged, tracked, and managed using tools like JIRA, ensuring they are addressed before release.
– JIRA for bug tracking and project management. – TestRail for test case management and reporting. – Postman for API testing. – Knowledge of manual testing methodologies and test execution.
– Small to medium-sized applications. – Early-stage projects where the product’s user interface and usability need detailed testing. – Projects that require human intuition for exploring new features and identifying potential user experience issues.
A Software Development Engineer in Test (SDET) is a specialized role that bridges the gap between development and testing. SDETs focus on test automation, creating frameworks and tools that ensure continuous testing across various stages of the Software Development Life Cycle (SDLC). They possess strong software development skills and are heavily involved in CI/CD pipelines, ensuring that quality is maintained at every stage of the development process.
– Test Automation: SDETs write automated test scripts for unit tests, integration tests, UI tests, and performance tests. Automation significantly speeds up testing cycles and ensures comprehensive test coverage. – CI/CD Integration: SDETs are involved in setting up and maintaining Continuous Integration (CI) and Continuous Delivery (CD) pipelines. They ensure that automated tests are executed whenever code is integrated, allowing for fast feedback. – Building Test Frameworks: SDETs develop reusable test frameworks that can be applied across different projects, making it easier to scale testing as the application grows. – Performance and Load Testing: They also conduct performance tests, stress tests, and load tests to ensure the application can handle high traffic and remains stable under peak loads. – Shift-Left Testing: SDETs work alongside developers to shift testing earlier in the SDLC, allowing defects to be identified and fixed earlier in the development process, which reduces costs and speeds up time-to-market.
– Automation Tools: Selenium, Cypress, Playwright, Appium for automating UI and API tests. – CI/CD Tools: Jenkins, GitLab CI, CircleCI, Travis CI for integrating tests into the development pipeline. – Languages: Proficiency in programming languages like JavaScript, Python, Java, and C#. – Containerization: Tools like Docker and Kubernetes for creating test environments and ensuring tests run in consistent conditions.
– Large, complex applications where manual testing becomes inefficient. – High-velocity teams in Agile or DevOps environments, where quick releases and continuous testing are necessary. – Applications that require extensive automated regression, load, and performance testing.
The importance of each role largely depends on the nature of the project and the testing strategy adopted by the organization. – SDETs are crucial in large-scale, fast-paced environments, especially with frequent code changes and deployments. They enable continuous testing and feedback, which is essential in Agile and DevOps settings. Automation not only saves time but also increases test coverage, ensuring that defects are caught early in the development process. – QA Analysts remain invaluable for manual testing, especially in validating user experience, UI consistency, and edge-case scenarios that may be difficult to automate. Conclusion: Both SDET and QA Analyst roles are essential for delivering high-quality software. While the SDET role is focused on automation and scalability, the QA Analyst role ensures that the product is user-friendly and meets functional specifications. The key to success lies in the collaboration between these two roles, ensuring that software is thoroughly tested, performs well, and delivers a seamless experience to users.
The research paper explores the Online shopping safety as it becomes a part and parcel in individual’s daily life with the rapid development of e- commerce. From buying convenience to logistics support, from product quality to customers service, these factors shape the customer satisfaction and loyalty towards online shopping. This study aims to explore the online shopping safety during 2020-2024 and also analyze the research gap through the systematic literature review. The research finding also suggest the future research according to the study. In this study, a systematic literature review was employed involving the search for the key word “online shopping safety” “online purchase intention” “online shopping risk” and “e-commerce experience” within the titles, abstracts and keyword of publication in the database. PRISMA frame work was employed to collect and analyze data with the objective of research questions. At the first stage of identification there are 75 abstract documents present their relation to the online shopping safety. Furthermore, the researcher selected 51 journals after reading the abstracts at the screening stage. Moreover, _ articles covering to the issue of online shopping safety and studied full text at eligibility stage. At the last stage the researcher included _ articles for depth analysis using a systematic literature review. This systematic review contributes to the arena by offering in depth insight to the businessman and government stakeholder so that they can apply effective strategy for the online shopping safety and it will maximize the relationship between the ultimate consumer and businessman. more
This paper investigates whether educational quality assurance models effectively address the quality of classroom management and school engagement. It examines practices within selected international and Bangladeshi universities, categorized by educational expense, to evaluate how quality assurance frameworks integrate indicators of classroom management and student engagement. Data were analyzed from online reports and university websites to assess the alignment of institutional policies with practical classroom strategies. The findings reveal gaps in integrating classroom-focused metrics into broader quality assurance activities, highlighting the need for frameworks that bridge institutional objectives with direct classroom practices. The originality of this study lies in its comparative analysis of diverse universities, shedding light on the role of financial capacities in shaping quality assurance initiatives. By emphasizing classroom dynamics and engagement as pivotal elements of educational excellence, this research contributes to the evolving discourse on quality assurance. The implications extend to policymakers and educators, suggesting actionable recommendations for developing comprehensive quality assurance systems that prioritize holistic student outcomes and foster innovation in classroom practices more
Searching for specific data in a database with hundreds of tables can be a challenge. This guide demonstrates how to dynamically search all tables in SQL Server for a specific value using a script. This approach is useful when you don’t know which table or column contains the data.
Below is a complete script you can use to search for any value across all tables and columns in your database. It works for text and GUIDs (uniqueidentifier
), handles missing tables, and avoids syntax issues.
DECLARE @SearchValue NVARCHAR(255) = 'your_search_value'; -- Replace with your value
DECLARE @SQL NVARCHAR(MAX) = '';
DECLARE @TempSQL NVARCHAR(MAX) = '';
-- Dynamically build the SQL query for all text-based and uniqueidentifier columns
SELECT @TempSQL = (
SELECT
CASE
WHEN OBJECT_ID(TABLE_SCHEMA + '.' + TABLE_NAME, 'U') IS NOT NULL THEN
'SELECT ''' + TABLE_SCHEMA + '.' + TABLE_NAME + ''' AS TableName, ''' + COLUMN_NAME + ''' AS ColumnName, ' +
'[' + COLUMN_NAME + '] AS MatchFound ' +
'FROM [' + TABLE_SCHEMA + '].[' + TABLE_NAME + '] ' +
'WHERE [' + COLUMN_NAME + '] LIKE ''%' + @SearchValue + '%'' UNION ALL '
ELSE ''
END
FROM INFORMATION_SCHEMA.COLUMNS
WHERE DATA_TYPE IN ('char', 'varchar', 'text', 'nchar', 'nvarchar', 'ntext', 'uniqueidentifier')
FOR XML PATH(''), TYPE
).value('.', 'NVARCHAR(MAX)');
-- Remove the trailing 'UNION ALL'
IF LEN(@TempSQL) > 10
SET @SQL = LEFT(@TempSQL, LEN(@TempSQL) - 10);
-- Execute the dynamically generated SQL if valid
BEGIN TRY
IF @SQL <> ''
EXEC sp_executesql @SQL;
ELSE
PRINT 'No matching data found.';
END TRY
BEGIN CATCH
PRINT 'An error occurred, but it was ignored: ' + ERROR_MESSAGE();
END CATCH;
char
, varchar
, nvarchar
, text
, ntext
, or uniqueidentifier
.OBJECT_ID
to ensure tables exist before querying them, avoiding errors from missing tables or views.TRY...CATCH
block ensures that any errors (e.g., syntax issues or inaccessible tables) are safely ignored.UNION ALL
and skips non-existent objects.Introduction:
In today’s increasingly interconnected world, software systems are central to the functioning of businesses, governments, and industries. Many of these systems, such as financial applications, healthcare systems, defense technologies, and critical infrastructure, handle sensitive data or control essential processes. For such systems, security is paramount. A security breach can lead to data loss, financial damage, compromised operations, or even loss of life. Therefore, ensuring the security of critical systems through rigorous testing is an essential component of the software development lifecycle.
What is Security Testing?
Security testing is the process of evaluating a software application or system to identify vulnerabilities, weaknesses, or threats that could lead to unauthorized access, data leakage, or manipulation. It aims to protect the system from malicious attacks, prevent data breaches, and ensure that sensitive information remains secure.
Security testing for critical systems involves assessing how the software behaves in the presence of malicious actors, incorrect usage, or unexpected inputs, and ensuring that the system meets required security standards and compliance regulations.
Key Objectives of Security Testing for Critical Systems:
Types of Security Testing for Critical Systems:
Best Practices for Security Testing in Critical Systems:
Challenges in Security Testing for Critical Systems:
Conclusion:
Security testing for critical systems is a vital part of software testing. It ensures that software is resilient to cyber threats, protecting both sensitive data and the integrity of the system. Given the potential consequences of security failures, organizations must adopt a comprehensive and proactive approach to security testing, integrating it early into the development lifecycle, using the latest tools and techniques, and ensuring continuous monitoring. By doing so, they can minimize the risk of cyber-attacks, maintain the trust of their users, and meet regulatory compliance requirements, all while safeguarding the functionality and security of critical systems.
📅 Date: November 1, 2024
🕒 Time: 3 PM – 4 PM
📍 Hosted by IT Magnet
Are you ready to elevate your automation testing skills? Join me, Rony Barua, an experienced SQA Lead, as I host an exclusive webinar on Automation Testing with C#. This session is designed to guide both newcomers and seasoned professionals through the transformative potential of Visual Studio for automation testing. Whether you’re looking to refine your skills or gain insights into the latest techniques, this webinar is the perfect opportunity!
Automation testing is essential in today’s fast-paced development environment. By mastering Visual Studio as a testing tool, you’re not only improving your skills but also contributing to the efficiency and reliability of your projects.